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• Evaluating Federated Learning Under Data Heterogeneity: Failure Modes and Practical Mitigations 
  Abstract Federated Learning (FL) allows for collaborative model training across decentralized clients while maintaining data privacy; yet real-world deployments always include heterogeneous and non-identically distributed (non-IID) client data. Existing FL res…
  europepmc Jyotiprakash Panda, Om Patil 2026 置信度 0.80
• Mitigating request flooding attack in named data networking using federated learning.
  Named Data Networking (NDN) represents a paradigm shift toward content-centric architectures but remains critically vulnerable to Interest Flooding Attacks (IFAs), where malicious actors overwhelm router Pending Interest Tables with spurious requests, causing …
  pubmed Benmaidi ML, Lagraa N, Brik B, Jlali L 2026 置信度 0.82
• A Durable Backdoor Attack on Medical Imaging via Federated Learning.
  Federated Learning (FL) enables multiple healthcare institutions to jointly train models without sharing raw patient data, making it a natural fit for privacy-sensitive medical applications. However, its distributed and partially trusted nature exposes it to b…
  pubmed Faraoun H, Bellafqira R, Coatrieux G, Kallas K 2026 置信度 0.82
• Federation Opacity and the Promise of Federated Learning in Healthcare.
  europepmc 2026 置信度 0.80
• FedHSFV: Federated Learning for Finger Vein Recognition via Hierarchical Decoupling and Subspace Metric.
  Finger vein recognition (FVR) has significant potential in biometrics due to its high accuracy and intrinsic liveness detection capabilities. However, the increasingly stringent privacy regulations have presented severe data security challenges for traditional…
  pubmed Zhou X, Wang Y, Cui J, Guo J 等 2026 置信度 0.82
• Privacy-preserving federated learning for interpretable student at-risk prediction across schools.
  Early-warning systems for at-risk students increasingly rely on predictive models trained on sensitive educational records. However, centralized learning pipelines raise concerns about privacy, institutional data sovereignty, and auditability, particularly whe…
  pubmed Jodayree M, Ghafi AK, Atashafrouz M, Shafiabadi MH 2026 置信度 0.82
• BLEND blockchain and federated learning enabled data sharing network.
  Combining blockchain and federated learning has emerged as a promising solution for secure, privacy-preserving data sharing and collaborative training of machine learning models in decentralised settings. However, their methods suffer from scalability issues, …
  pubmed Sudhakar G, Reddy MI, Pradeep KR, Madhavi GB 等 2026 置信度 0.82
• FLVaccin: Unbalanced Hierarchical Federated Learning with Vaccination-Calibrated Adaptive Quarantine for Robust Poisoning Defense
  europepmc 2026 置信度 0.80
• Privacy-Enhanced Federated Learning Model for Secure Internet of Things Environments
  europepmc 2026 置信度 0.80
• Boosting the Performance of Decentralized Federated Learning via Catalyst Acceleration.
  Decentralized Federated Learning has emerged as an alternative to centralized architectures due to its faster training, privacy preservation, and reduced communication overhead. In decentralized communication, the server aggregation phase in Centralized Federa…
  pubmed Li Q, Zhang M, Liu Y, Yin Q 等 2026 置信度 0.82
• Secure adaptive federated learning for scalable anomaly detection in industrial IoT networks.
  The rapid expansion of Industrial Internet of Things (IIoT) infrastructures has increased the demand for robust, scalable, and privacy-preserving intrusion and anomaly detection mechanisms. Traditional centralized detection systems face critical limitations, i…
  pubmed Alatawi MN 2026 置信度 0.82
• Stability-Controlled Continual Federated Learning for Energy-Harvesting AIoT Systems.
  Energy-harvesting (EH) AIoT systems enable long-term autonomous operation but suffer from time-varying energy availability, which makes stable learning difficult. In such environments, federated learning (FL) is prone to energy depletion (blackout), while cont…
  pubmed Park J, Yoon I, Noh DK 2026 置信度 0.82
• Center-specific Federated Learning for Radiation Pneumonitis: A Cross-Center Adaptive Alternating Framework.
  Accurate prediction of symptomatic radiation pneumonitis (RP) is critical for radiation therapy, however, the generalization of deep learning models is hindered by restricted access to multicenter data. Although federated learning (FL) bypasses data sharing re…
  pubmed Yan M, Wang Z, Ning L, Xuan J 等 2026 置信度 0.82
• Clinical Equivalence of Privacy-Preserving Federated Learning: A Consortium Blockchain Framework with Equivalence Testing
  europepmc 2026 置信度 0.80
• FedEBM: Robust graph federated learning via energy-based model.
  Graph Federated Learning (GFL), as a vital component of graph neural networks, has found extensive applications in real-world scenarios. However, real-world graph data often suffers from label noise due to factors such as mislabeled data or malicious attacks. …
  pubmed Wang J, Gan Z, Li X, Li D 2026 置信度 0.82
• Federated Learning for Distributed Cyber Threat Detection: A Systematic Review of Architectures, Applications, and Open Challenges
  Abstract The proliferation of networked infrastructure, spanning enterprise information systems, Internet of Things (IoT) deployments, and industrial control environments, has widened the attack surface available to adversaries and made purely centralised intr…
  europepmc Joshua Babatola 2026 置信度 0.80
• Federated learning with swarm intelligence for efficient and secure medical image analysis.
  Collaborative learning in healthcare faces challenges, including strict regulations and fragmented data. This research introduces a federated learning framework that employs swarm intelligence to augment communication and enhance the analysis of medical images…
  pubmed SayedElahl MA, Farouk RM, Ali AE, Ahmed E 2026 置信度 0.82
• Flexible and scalable federated learning with deep feature prompts for digital pathology.
  Collaborative learning across medical institutions is essential for building robust and generalisable digital pathology models. Federated learning (FL) enables collaboration without centralising data, yet its adoption is limited by high communication costs, mo…
  pubmed Cong C, Song Y, Di Ieva A, Chou A 等 2026 置信度 0.82
• Fed-DiTTab: Diffusion transformer for tabular data generation in federated learning.
  Imbalanced data is prevalent in real-world classification tasks, and traditional machine learning methods often struggle to effectively learn from minority class samples. This implies that predictive models can achieve better performance only when sufficient a…
  pubmed Pi Y, Zheng M, Ma F 2026 置信度 0.82
• BlockFedZTA: a trust-aware federated learning framework for secure multi-organizational intrusion detection.
  The design of a privacy-preserved intrusion detection system for supply chain networks is challenging because of strict data privacy requirements, heterogeneous data distributions, and unreliable participating nodes. This study proposes BlockFedZTA, a framewor…
  pubmed Alshenaifi R, Mishra S, Tahzib S, Rathi M 2026 置信度 0.82
• FedCAD: Cross-modal semantic alignment and distillation for cross-domain heterogeneous federated learning.
  Recent advances in the IoT and edge intelligence have made the deployment of CLIP-style image-text models in edge-cloud architectures increasingly common for collaborative sensing. However, resource heterogeneity at the edge limits the feasibility of using a u…
  pubmed Zhang Q, Yuan H, Shao M, Liang H 等 2026 置信度 0.82
• Real-world federated learning for brain imaging scientists.
  Background Federated learning (FL) has the potential to boost deep learning in neuroimaging but is rarely deployed in real-world scenarios, where its true potential lies. We propose FLightcase, a new FL toolbox tailored for brain research, and evaluate it on a…
  europepmc 2026 置信度 0.80
• Multi-scenario evaluation of federated learning for privacy-preserving malaria prediction with Ghana DHS data.
  Improving malaria prediction in Ghana requires data from across its health system, yet Ghana's Data Protection Act (Act 843) restricts inter-institutional data sharing, and many facilities decline to transfer patient records regardless of legal permission. Fed…
  pubmed Kovor DK, Osei EO 2026 置信度 0.82
• GBOA_SR-ShuffleNet: an explainable federated learning framework for privacy-preserving intrusion detection in IoT.
  The rapid growth of the Internet of Things (IoT) has introduced significant security vulnerabilities and increased the risk of cyberattacks. Intrusion Detection Systems (IDS) are widely used to identify malicious activities; however, their detection accuracy i…
  pubmed Jacob SL, Sultana HP 2026 置信度 0.82
• A Method for Data Augmentation in Vertical Federated Learning Addressing Data Heterogeneity.
  Vertical federated learning (VFL) can aggregate data features from participating parties and is applicable to data collaboration in various fields. To address data heterogeneity in VFL, this article proposes a framework tailored for heterogeneous environments.…
  pubmed Xiao Y, Lv T, Zhao D, Zhao W 等 2026 置信度 0.82
• SecureTrust-FL: trust-aware privacy-preserving federated learning for network intrusion detection.
  The proliferation of distributed network environments and the Internet of Things (IoT) has increased the need for privacy-preserving intrusion detection systems capable of operating effectively under heterogeneous and non-independent and identically distribute…
  pubmed Alshammari NS, Mishra S, Rathi M, Goel N 等 2026 置信度 0.82
• VFLING: Vertical Federated Learning for Multi-Omics Data Integration with Graphs.
  Modern machine learning models leveraging multi-omics data face significant privacy challenges due to the sensitive nature of patient information. Communication overhead and missing features in each institution can lead to a substantial decline in federated le…
  pubmed Li X, Li Q, Lu D, Lin Y 等 2026 置信度 0.82
• TrainTracks - federated learning for reproducible research on sensitive medical data.
  Reproducibility of computational algorithms is a challenging but crucial requirement for medical research and an important component of trustworthy training and application of AI algorithms. Federated Learning (FL) is commonly used to enable privacy-preserving…
  pubmed Elwes M, Jaberansary M, Tang FK, Aswendt M 等 2026 置信度 0.82
• BRIDGE-T: addressing temporal unreliability in federated learning for edge-enabled IoT networks.
  Federated Learning (FL) in edge-enabled Internet of Things (IoT) networks faces considerable challenges owing to intermittent client participation and distributional drift, and which undermines the stability of a global model's optimization. This coupled impac…
  pubmed Islam F, Mahmood A, Wang Y, Tahermazandarani M 2026 置信度 0.82
• FedGSCA: Medical Federated Learning With Global Sample Selector and Client Adaptive Adjuster Under Label Noise.
  europepmc 2026 置信度 0.80
• Reliability-aware modality completion with cross-modal distillation for federated learning with missing modalities.
  Multimodal federated learning (MFL) enables multiple clients to collaboratively train a global model from decentralized data without sharing local privacy-sensitive information. However, practical MFL is challenged by cross-client heterogeneity and missing mod…
  pubmed Li M, He X, Chen J 2026 置信度 0.82
• Privacy-Enhanced Vertical Federated Learning for Healthcare via Directional Noise and Subset Representations.
  Vertical federated learning (VFL) allows healthcare institutions to train models on complementary patient features without sharing raw data, but strong differential privacy often causes severe utility loss and labeled medical data are limited.We propose HEAL, …
  pubmed Wang Q, Dai M, Wu C 2026 置信度 0.82
• Stable and Budget-Feasible Coalition Formation for Clustered Federated Learning: A Hedonic Potential-Game Approach
  Abstract Federated learning systems can benefit from organizing heterogeneous participants into coalitions that train coalition-specific models. Such clustering is sustainable only if participants prefer to remain in their assigned coalition and the associated…
  europepmc Cengis Hasan 2026 置信度 0.80
• Intelligent Federated Learning Framework for Non-colocated and Heterogeneous Datasets.
  Federated learning has significant potential for distributed model training while preserving privacy, but it faces challenges related to convergence, fairness, and interpretability due to the heterogeneity of non-colocated datasets. This study proposes an Inte…
  pubmed Navghare ND, Gladence LM, Bhosle AA, Gore R 2026 置信度 0.82
• Modality-Agnostic Federated Learning With Adaptive Updates for Heterogeneous Medical Image Tasks.
  europepmc 2026 置信度 0.80
• Adaptive client participation mechanism for federated learning in heterogeneous vehicular networks.
  In vehicular networks, federated learning faces significant challenges due to resource heterogeneity, dynamic participation patterns, and intermittent connectivity among vehicles. Traditional client selection mechanisms often fail to consider the two-tier deci…
  pubmed Lin W, Zhou Y 2026 置信度 0.82
• Secure Aggregation Techniques in Federated Learning for Vehicle Data Analytics
  The rapid evolution of Intelligent Transportation Systems (ITS) and Autonomous Vehicles (AVs) has generated a massive influx of vehicular data. While this data is pivotal for enhancing traffic safety and predictive maintenance, privacy concerns regarding locat…
  europepmc Emily Brown, Sarah Jones, David Miller, Grace Elvis 2026 置信度 0.80
• FL-TWIN: a unified federated learning system for intrusion detection with digital twins modelling.
  The growth of networked environments has intensified the challenge of detecting distributed denial-of-service (DDoS) attacks, as centralized intrusion detection systems face scalability, privacy, and data heterogeneity limitations. This paper proposes a federa…
  pubmed Hamwi AA, Mittal M 2026 置信度 0.82
• Good for All, Not Good Enough for One: Reuse Dilemma in Federated Learning.
  Federated learning (FL) promises privacy-aware collaboration in healthcare, but real-world adoption remains limited by infrastructural and organizational hurdles. In this paper, we reflect on our experience developing and later bypassing our own general-purpos…
  pubmed Pirmani A, Moreau Y, Peeters LM 2026 置信度 0.82
• Embodied brain-cerebellum federated learning for satellite-assisted low-altitude wireless networks
  Abstract Satellite-assisted low-altitude wireless networks need distributed intelligence that handles scarce labels, limited onboard resources and short UAV contact windows. We propose EBC-FL, an embodied brain--cerebellum federated learning framework in which…
  europepmc Yi Jing, Chunxiao Jiang, Jiawei Wang, Jiachen Sun 2026 置信度 0.80
• Interpretable reputation driven asynchronous consensus vehicle networking federated learning architecture.
  This paper proposes a Vehicle-Road-Cloud-Chain (VRCC) four-layer collaborative framework to address the issues of lack of interpretability, reputation evaluation failure, and architecture centralization vulnerability faced by federated learning in Internet of …
  pubmed Zi Y, Zhou Y 2026 置信度 0.82
• Federated learning: global insights from local plant data.
  pubmed Shoaib M, Reddy P, Long G, Hayden MJ 等 2026 置信度 0.82
• A unified basis decomposition framework for addressing the federated learning trilemma: Communication-efficiency, personalization, and privacy.
  Federated learning (FL) is confronted with a fundamental trilemma: simultaneously achieving communication efficiency, personalized adaptation, and privacy protection. Current approaches typically optimize one objective at the expense of the others, failing to …
  pubmed Ma Z, Wu Z, Wang J, Zhu Y 等 2026 置信度 0.82
• Quantifying energy and accuracy trade-offs of federated learning on wearable health devices.
  The rapid development of wearable health tools has made it possible to continuously monitor physiological conditions for preventive care. However, stringent privacy laws, including HIPAA and GDPR, require decentralized methods such as federated learning (FL) t…
  pubmed S R, Khekare G, Kumar Y, Soni G 2026 置信度 0.82
• A federated learning framework integrating knowledge graphs and Node2Vec for multi-source medical image classification.
  Medical image classification in federated healthcare environments is challenged by the difficulty of learning robust and generalized representations from heterogeneous, non-IID data distributed across multiple institutions. Conventional federated learning appr…
  pubmed Alqarni AA 2026 置信度 0.82
• CAREFL: Context-Aware Recognition of Emotions with Federated Learning
  europepmc 2026 置信度 0.80
• Stateful Abstractions of Secure Multiparty Computation
  In this chapter we present the Arithmetic Black Box (ABB) functionality. It is an ideal functionality that preserves the privacy of the data it stores and allows computations to be performed on the data stored, as well as retrieve certain pieces of data. We sh…
  crossref Laud Peeter 2025-02-20T09:38:17Z 置信度 0.70
• Practical Applications of Secure Multiparty Computation
  As secure multiparty computation technology becomes more mature, we see more and more practical applications where computation moves from a lab setting to an actual deployment where each computation node is hosted by a different party. In this chapter, we pres…
  crossref Talviste Riivo 2025-02-20T09:38:17Z 置信度 0.70
• Oblivious Array Access for Secure Multiparty Computation
  In this chapter, we describe efficient protocols for performing reads and writes in private arrays according to private indices. The protocols are implemented on top of the arithmetic black box (ABB) and can be composed freely to build larger privacy-preservin…
  crossref Laud Peeter 2025-02-20T09:38:17Z 置信度 0.70
• Information security - Secure multiparty computation
  crossref 2023-08-03T20:30:33Z 置信度 0.70
• Secure Multiparty Computation Methods
  crossref 2011-08-29T12:52:50Z 置信度 0.70
• Social Need for Secure Multiparty Computation
  The aim of this chapter is to introduce and discuss the potential socio-technical barriers that might be hindering the adoption of Secure Multiparty Computation (SMC) techniques. By investigating the conditions of adoption of technology under development, we a…
  crossref Kanger Laur, Pruulmann-Vengerfeldt Pille 2025-02-20T09:38:17Z 置信度 0.70
• Business Process Engineering and Secure Multiparty Computation
  In this chapter we use secure multiparty computation (SMC) to enable privacy-preserving engineering of inter-organizational business processes. Business processes often involve structuring the activities of several organizations, for example when several poten…
  crossref Guanciale Roberto, Gurov Dilian, Laud Peeter 2025-02-20T09:38:17Z 置信度 0.70
• Basic Constructions of Secure Multiparty Computation
  In this chapter, we formally define multiparty computation tasks and the security of protocols realizing them. We give a broad presentation of the existing constructions of secure multiparty computation (SMC) protocols and explain why they are correct and secu…
  crossref Laud Peeter, Pankova Alisa, Kamm Liina, Veeningen Meilof 2025-02-20T09:38:17Z 置信度 0.70
• Efficient delegated secure multiparty computation
  Délégation efficace de calcul multipartite sécurisé Avec l’essor du cloud, il est devenu plus simple de déléguer la gestion et l’analyse des données à des infrastructures externes, favorisant la combinaison de données variées pour en tirer des informations uti…
  crossref Antoine Urban 2026-04-09T02:18:47Z 置信度 0.70
• Statistical Analysis Methods Using Secure Multiparty Computation
  This chapter gives an overview of privacy-preserving versions of the analysis methods and algorithms that are most commonly used in statistical analysis. We discuss methods for data collection and sharing, and describe privacy-preserving database joins and sor…
  crossref Kamm Liina, Bogdanov Dan, Pankova Alisa, Talviste Riivo 2025-02-20T09:38:17Z 置信度 0.70
• Sublinear-communication secure multiparty computation
  Calcul multipartite sécurisé avec communication sous-linéaire Le calcul multipartite sécurisé (en anglais, MPC) [Yao82,GMW87a] permet à des agents d'un réseau de communication de calculer conjointement une fonction de leurs entrées sans avoir à n'en rien révél…
  crossref Pierre Meyer 2026-04-07T17:36:28Z 置信度 0.70
• Preliminaries
  crossref 2015-08-05T05:01:30Z 置信度 0.70
• Post-Quantum Signatures from Secure Multiparty Computation
  Signatures post-quantiques à partir de techniques de calcul multipartite Le développement actuel des ordinateurs quantiques pousse la communauté cryptographique à mettre au point de nouveaux cryptosystèmes dont la sécurité se fonde sur la difficulté à résoudre…
  crossref Thibauld Feneuil 2026-04-08T09:14:12Z 置信度 0.70
• Introduction
  crossref 2015-08-05T05:01:30Z 置信度 0.70
• References
  crossref 2015-08-05T05:01:30Z 置信度 0.70
• Preface
  crossref 2015-08-05T05:01:30Z 置信度 0.70
• Zero-knowledge arguments from secure multiparty computation
  Arguments à divulgation nulle de connaissance via du calcul réparti sécurisé Cette thèse a pour but d'étudier les arguments à divulgation nulle de connaissance, une primitive cryptographique qui permet de prouver un énoncé tout en ne révélant rien d'autre que …
  crossref Jules Maire 2026-04-08T20:21:09Z 置信度 0.70
• Models
  crossref 2015-08-05T05:01:30Z 置信度 0.70
• Secure Multiparty Computation of the Laplace Mechanism
  crossref Amir Zarei, Staal Vinterbo 2024-03-01T18:44:53Z 置信度 0.70
• Verifiable Computation in Multiparty Protocols with Honest Majority
  We present a generic method for turning passively secure protocols into protocols secure against covert attacks. This method adds to the protocol a post-execution verification phase that allows a misbehaving party to escape detection only with negligible proba…
  crossref Pankova Alisa, Laud Peeter 2025-02-20T09:38:17Z 置信度 0.70
• Multiparty Secure Computation
  crossref 2023-03-23T00:07:28Z 置信度 0.70
• Privacy-preserving data mining via secure multiparty computation
  crossref Shu Guo Han 2019-10-02T11:39:33Z 置信度 0.70
• Algebraic Preliminaries
  crossref 2015-08-05T05:01:30Z 置信度 0.70
• Secret Sharing
  crossref 2015-08-05T05:01:30Z 置信度 0.70
• Information-Theoretic Secure Multiparty Computation
  Most of this book has considered two-party and multiparty computation with security with (unfair) abort. This security notion allows the adversary to force the honest parties to abort even depending on the outputs of corrupted parties. (Note, however, that the…
  crossref Maurer Ueli, Zikas Vassilis 2025-02-20T09:38:17Z 置信度 0.70
• Achieving Optimal Utility for Distributed Differential Privacy Using Secure Multiparty Computation
  Computing aggregate statistics about user data is of vital importance for a variety of services and systems, but this practice seriously undermines the privacy of users. Recent research efforts have focused on the development of systems for aggregating and com…
  crossref Eigner Fabienne, Kate Aniket, Maffei Matteo, Pampaloni Francesca 等 2025-02-20T09:38:17Z 置信度 0.70
• Arithmetic Codices
  crossref 2015-08-05T05:01:30Z 置信度 0.70
• Applications of MPC
  crossref 2015-08-05T05:01:30Z 置信度 0.70
• Integrating Secure Multiparty Computation into Data Spaces
  crossref Veronika Siska, Thomas Lorünser, Stephan Krenn, Christoph Fabianek 2024-05-06T16:53:02Z 置信度 0.70
• Quantum Secure Multiparty Computation based on Secure Summation and QKD
  Abstract Secure Multiparty Computation (MPC) is a cryptography technique that allows multiple parties to securely perform operations on their private inputs without revealing them. It is used in various applications, such as data aggregation in the cloud, IoT,…
  europepmc Mandeep Kumar, Bhaskar Mondal 2025 置信度 0.80
• Cryptographic MPC Protocols
  crossref 2015-08-05T05:01:30Z 置信度 0.70
• The BGW Protocol for Perfectly-Secure Multiparty Computation
  One of the most fundamental results of secure computation was presented by Ben-Or, Goldwasser and Wigderson (BGW) in 1988. They demonstrated that any n-party functionality can be computed with perfect security, in the private channels model. When the adversary…
  crossref Asharov Gilad, Lindell Yehuda 2025-02-20T09:38:17Z 置信度 0.70
• Privacy in Optimization Algorithms based on Secure Multiparty Computation
  crossref Katrine Tjell 2022-06-01T11:28:40Z 置信度 0.70
• Secure multiparty computation solutions of collection member decision
  crossref Yongli DOU, Haichun WANG, Jian KANG 2013-12-17T08:09:09Z 置信度 0.70
• Efficient Constructions of Non-interactive Secure Multiparty Computation from Pairwise Independent Hashing
  crossref Satoshi Obana, Maki Yoshida 2020-07-15T15:30:18Z 置信度 0.70
• Privacy preserving query processing on outsourced data via secure multiparty computation
  crossref Hoang Giang Do 2020-10-28T06:55:20Z 置信度 0.70
• Information-Theoretic Robust MPC Protocols
  crossref 2015-08-05T05:01:30Z 置信度 0.70
• Some Techniques for Efficiency Improvements
  crossref 2015-08-05T05:01:30Z 置信度 0.70
• MPC Protocols with Passive Security
  crossref 2015-08-05T05:01:30Z 置信度 0.70
• Secure Multiparty Computation
  crossref 2011-10-27T09:52:10Z 置信度 0.70
• Secure Multiparty Computation in Real Numbers
  crossref Jaron Skovsted Gundersen, Katrine Tjell, Rafael Wisniewski 2023-06-30T17:18:07Z 置信度 0.70
• Secure Comparisons of Single Nucleotide Polymorphisms Using Secure Multiparty Computation
  Abstract While genomic variations can provide valuable information for healthcare and ancestry, the privacy of individual genomic data must be protected. Thus, a secure environment is desirable for a human DNA database such that the total data are queryable bu…
  europepmc Andrew Woods, Skyler Kramer, Dong Xu, Wei Jiang 2022 置信度 0.80
• MPC from General Linear Secret-Sharing Schemes
  crossref 2015-08-05T05:01:30Z 置信度 0.70
• Design of a Real-Time Secure Multiparty Computation Protocol for Distributed Financial Networks
  crossref Prerna Dusi, Manjulata Bhoi 2026-03-04T10:47:33Z 置信度 0.70
• Secure Multiparty Computation
  crossref Sahana D. Gowda 2024-07-17T00:03:40Z 置信度 0.70
• Secure Multiparty Computing Protocol
  Secure Multiparty Computation (SMC) can be defined as n number of parties who do joint computation on their inputs (x1, x2…xn) using some function F and want output in the form of y. The increase in sensitive data on a network raises concern about the security…
  crossref Zulfa Shaikh, Poonam Garg 2013-06-27T16:45:37Z 置信度 0.70
• Privacy-Preserving Distributed Computation
  crossref Jonathan Katz 2024-08-23T14:36:41Z 置信度 0.70
• Secure Multiparty Computation (SMC)
  crossref Keith B Frikken 2011-10-27T09:52:10Z 置信度 0.70
• Secure Multiparty Computation Methods
  crossref Murat Kantarcıoǧlu, Jaideep Vaidya 2009-09-16T08:05:42Z 置信度 0.70
• Transformation-based Computation and Impossibility Results
  In this chapter we study transformation-based approaches for outsourcing some particular tasks, based on publishing and solving a transformed version of the problem instance. First, we demonstrate a number of attacks against existing transformations for privac…
  crossref Pankova Alisa, Laud Peeter 2025-02-20T09:38:17Z 置信度 0.70
• Trust Enabled Secure Multiparty Computation
  crossref Renren Dong, Ray Kresman 2010-09-15T21:15:39Z 置信度 0.70
• Secure Multiparty Computation and Secret Sharing
  In a data-driven society, individuals and companies encounter numerous situations where private information is an important resource. How can parties handle confidential data if they do not trust everyone involved? This text is the first to present a comprehen…
  crossref Ronald Cramer, Ivan Bjerre Damgård, Jesper Buus Nielsen 2015-08-05T05:01:30Z 置信度 0.70
• Residential Flexibility Characterization and Trading Using Secure Multiparty Computation
  crossref Fairouz Zobiri, Mariana Gama, Svetla Nikova, Geert Deconinck 2023-06-16T06:18:11Z 置信度 0.70
• Overview of Secure Multi-Party Computation Applications in Health Research and Social Sciences
  crossref Liina Kamm, Dan Bogdanov 2024-08-23T14:36:41Z 置信度 0.70