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• Data privacy in knowledge discovery
  This thesis addresses data privacy in various stages of extracting knowledge embedded in databases. Advances in computer networking and database technologies have enabled the collection and storage of vast quantities of data. Legal and ethical considerations m…
  datacite Jagannathan, Geetha 2010 置信度 0.66
• Privacy-preserving collaborative optimization
  With the rapid growth of computing, storing and networking resources, data is not only collected and stored, but also analyzed by different parties. This creates serious privacy problems while inhibiting the use of such distributed data. In turn, this raises t…
  datacite Hong, Yuan 2013 置信度 0.66
• Quantum-secure multiparty deep learning
  Secure multiparty computation enables the joint evaluation of multivariate functions across distributed users while ensuring the privacy of their local inputs. This field has become increasingly urgent due to the exploding demand for computationally intensive …
  datacite Sulimany, Kfir, Vadlamani, Sri Krishna, Hamerly, Ryan, Iyengar, Prahlad 等 2024 置信度 0.66 Quantum Physics (quant-ph)Artificial Intelligence (cs.AI)Information Theory (cs.IT)Machine Learning (cs.LG)Optics (physics.optics)
• Towards a Functionally Complete and Parameterizable TFHE Processor
  Fully homomorphic encryption allows the evaluation of arbitrary functions on encrypted data. It can be leveraged to secure outsourced and multiparty computation. TFHE is a fast torus-based fully homomorphic encryption scheme that allows both linear operations,…
  datacite Häusler, Valentin Reyes, Ott, Gabriel, Jayasena, Aruna, Peter, Andreas 2025 置信度 0.66 Cryptography and Security (cs.CR)FOS: Computer and information sciencesFOS: Computer and information sciences
• Enhancing Distributed Authorization With Lagrange Interpolation And Attribute-Based Encryption
  In todays security landscape, every user wants to access large amounts of data with confidentiality and authorization. To maintain confidentiality, various researchers have proposed several techniques. However, to access secure data, researchers use access con…
  datacite Sinha, Keshav, Sumitra, Kumari, Richa, Bhardwaj, Akashdeep 等 2025 置信度 0.66 Cryptography and Security (cs.CR)FOS: Computer and information sciencesFOS: Computer and information sciences
• Guard-GBDT: Efficient Privacy-Preserving Approximated GBDT Training on Vertical Dataset
  In light of increasing privacy concerns and stringent legal regulations, using secure multiparty computation (MPC) to enable collaborative GBDT model training among multiple data owners has garnered significant attention. Despite this, existing MPC-based GBDT …
  datacite Song, Anxiao, Cui, Shujie, Bai, Jianli, Cheng, Ke 等 2025 置信度 0.66 Cryptography and Security (cs.CR)FOS: Computer and information sciences
• vantage6
  Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains,…
  datacite Martin, Frank, Beusekom, Bart, Leurs, Richard, Sieswerda, Melle 等 2025 置信度 0.66 federated learningmachine learningdata analysissecure multiparty computationprivacy enhancing technology
• Thwarting Malicious Adversaries in Homomorphic Encryption Pipelines
  Homomorphic Encryption (HE) enables computations to be executed directly on encrypted data. As such, it is an auspicious solution for protecting the confidentiality of sensitive data without impeding its usability. However, HE does not provide any guarantees t…
  datacite Chatel, Sylvain 2023 置信度 0.66 Homomorphic EncryptionSecure Multiparty ComputationMalicious AdversaryProof Systems
• vantage6
  Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains,…
  datacite Martin, Frank, Beusekom, Bart, Leurs, Richard, Sieswerda, Melle 等 2025 置信度 0.66 federated learningmachine learningdata analysissecure multiparty computationprivacy enhancing technology
• PETs and AI: Privacy Washing and the Need for a PETs Evaluation Framework (Dagstuhl Seminar 25112)
  As public awareness of data collection practices and regulatory frameworks grows, privacy-enhancing technologies (PETs) have emerged as a promising approach to reconciling data utility with individual privacy rights. PETs underpin privacy-preserving machine le…
  datacite Cristofaro, Emiliano De, Shrishak, Kris, Strufe, Thorsten, Troncoso, Carmela 等 2025 置信度 0.66 Privacy Enhancing Technologies (PET)Privacy EvaluationPrivacy HarmPrivacy ThreatsPrivacy Washing
• Enhancing communication privacy using trustworthy remote entities
  Communication privacy is the property of a communication system that enables two or more distrusting participants to exchange information without compromising their privacy, with respect to internal and external adversaries. It encompasses aspects of anonymous…
  datacite Paverd, Andrew 2015 置信度 0.66 Computer scienceComputer security
• Privacy-Preserving Federated Analytics using Multiparty Homomorphic Encryption
  Analyzing and processing data that are siloed and dispersed among multiple distrustful stakeholders is difficult and can even become impossible when the data are sensitive or confidential. Current data-protection and privacy regulations highly restrict the sha…
  datacite Froelicher, David Jules 2021 置信度 0.66 federated analyticsfederated machine learningmultiparty homomorphic encryptiondifferential privacydecentralized systems
• Bridging the gap between theoretical and practical privacy technologies for at-risk populations
  With the pervasive digitalization of modern life, we benefit from efficient access to information and services. Yet, this digitalization poses severe privacy challenges, especially for special-needs individuals. Beyond being a fundamental human right, privacy …
  datacite Edalatnejadkhamene, Kasra 2023 置信度 0.66 Privacy enhancing technologiesapplied cryptographyprivacy engineeringhomomorphic encryptionprivate set intersection
• Security Issues in Cognitive Radio Networks
  Cognitive radio is an emerging trend to solve the problem of scarce spectrum resources in the prosperous area of wireless communication. By dynamically utilizing unoccupied spectrums of primary (licensed) users, secondary (unlicensed) users can meet their own …
  datacite Zhang, Tongjie 2014 置信度 0.66 Computer ScienceCognitive Radio NetworksSecurity
• vantage6
  Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains,…
  datacite Martin, Frank, Beusekom, Bart, Leurs, Richard, Sieswerda, Melle 等 2025 置信度 0.66 federated learningmachine learningdata analysissecure multiparty computationprivacy enhancing technology
• vantage6
  Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains,…
  datacite Martin, Frank, Beusekom, Bart, Leurs, Richard, Sieswerda, Melle 等 2025 置信度 0.66 federated learningmachine learningdata analysissecure multiparty computationprivacy enhancing technology
• The influence of decentralized AI architectures on distributed data governance
  The evolution of Artificial Intelligence (AI) from centralized models toward decentralized architectures has fundamentally reshaped the paradigms of data management, ownership, and governance. In traditional AI ecosystems, data is consolidated within centraliz…
  datacite Rashmi K. Nair 2020 置信度 0.66 Decentralized Artificial Intelligence; Federated Learning; Distributed Data Governance; Blockchain; Edge Intelligence; Data Sovereignty; Privacy Preservation; AI Ethics; Trust Frameworks; Autonomous Governance.
• The influence of decentralized AI architectures on distributed data governance
  The evolution of Artificial Intelligence (AI) from centralized models toward decentralized architectures has fundamentally reshaped the paradigms of data management, ownership, and governance. In traditional AI ecosystems, data is consolidated within centraliz…
  datacite Rashmi K. Nair 2020 置信度 0.66 Decentralized Artificial Intelligence; Federated Learning; Distributed Data Governance; Blockchain; Edge Intelligence; Data Sovereignty; Privacy Preservation; AI Ethics; Trust Frameworks; Autonomous Governance.
• The Influence Of Decentralized AI Architectures On Distributed Data Governance
  The evolution of Artificial Intelligence (AI) from centralized models toward decentralized architectures has fundamentally reshaped the paradigms of data management, ownership, and governance. In traditional AI ecosystems, data is consolidated within centraliz…
  datacite Rashmi K. Nair 2020 置信度 0.66
• The Influence Of Decentralized AI Architectures On Distributed Data Governance
  The evolution of Artificial Intelligence (AI) from centralized models toward decentralized architectures has fundamentally reshaped the paradigms of data management, ownership, and governance. In traditional AI ecosystems, data is consolidated within centraliz…
  datacite Rashmi K. Nair 2020 置信度 0.66
• vantage6
  Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains,…
  datacite Martin, Frank, Beusekom, Bart, Leurs, Richard, Sieswerda, Melle 等 2025 置信度 0.66 federated learningmachine learningdata analysissecure multiparty computationprivacy enhancing technology
• vantage6
  Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains,…
  datacite Martin, Frank, Beusekom, Bart, Leurs, Richard, Sieswerda, Melle 等 2025 置信度 0.66 federated learningmachine learningdata analysissecure multiparty computationprivacy enhancing technology
• vantage6
  Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains,…
  datacite Martin, Frank, Beusekom, Bart, Leurs, Richard, Sieswerda, Melle 等 2025 置信度 0.66 federated learningmachine learningdata analysissecure multiparty computationprivacy enhancing technology
• vantage6
  Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains,…
  datacite Martin, Frank, Beusekom, Bart, Leurs, Richard, Sieswerda, Melle 等 2025 置信度 0.66 federated learningmachine learningdata analysissecure multiparty computationprivacy enhancing technology
• vantage6
  Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains,…
  datacite Martin, Frank, Beusekom, Bart, Leurs, Richard, Sieswerda, Melle 等 2025 置信度 0.66 federated learningmachine learningdata analysissecure multiparty computationprivacy enhancing technology
• vantage6
  Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains,…
  datacite Martin, Frank, Beusekom, Bart, Leurs, Richard, Sieswerda, Melle 等 2025 置信度 0.66 federated learningmachine learningdata analysissecure multiparty computationprivacy enhancing technology
• vantage6
  Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains,…
  datacite Martin, Frank, Beusekom, Bart, Leurs, Richard, Sieswerda, Melle 等 2025 置信度 0.66 federated learningmachine learningdata analysissecure multiparty computationprivacy enhancing technology
• vantage6
  Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains,…
  datacite Martin, Frank, Beusekom, Bart, Leurs, Richard, Sieswerda, Melle 等 2025 置信度 0.66 federated learningmachine learningdata analysissecure multiparty computationprivacy enhancing technology
• vantage6
  Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains,…
  datacite Martin, Frank, Beusekom, Bart, Leurs, Richard, Sieswerda, Melle 等 2025 置信度 0.66 federated learningmachine learningdata analysissecure multiparty computationprivacy enhancing technology
• Performance of Practical Quantum Oblivious Key Distribution
  Motivated by the applications of secure multiparty computation as a privacy-protecting data analysis tool, and identifying oblivious transfer as one of its main practical enablers, we propose a practical realization of randomized quantum oblivious transfer. By…
  datacite Lemus, Mariano, Schiansky, Peter, Goulão, Manuel, Bozzio, Mathieu 等 2025 置信度 0.66 Quantum Physics (quant-ph)FOS: Physical sciencesFOS: Physical sciences
• PolyKervNets: Activation-free Neural Networks For Efficient Private Inference
  With the advent of cloud computing, machine learning as a service (MLaaS) has become a growing phenomenon with the potential to address many real-world problems. In an untrusted cloud environment, privacy concerns of users is a major impediment to the adoption…
  datacite Aremu, Toluwani Samuel 2022 置信度 0.66 Machine Learning
• Federated Distributed Key Generation
  Distributed Key Generation (DKG) underpins threshold cryptography in many systems, including decentralized wallets, validator key ceremonies, cross-chain bridges, threshold signatures, secure multiparty computation, and internet voting. Classical ($t$,$n$)-DKG…
  datacite Baranski, Stanislaw, Szymanski, Julian 2025 置信度 0.66 Cryptography and Security (cs.CR)FOS: Computer and information sciencesFOS: Computer and information sciencesE.3; C.2
• Attack Once, Compromise All? On the Scalability of Attacks
  Electronic voting schemes are often criticized for being insecure, on the grounds that a successful attack would allow an adversary to manipulate all votes at once. It is argued that attacks therefore have a higher impact at lower adversary costs compared to p…
  datacite Hetzel, Eva, Nemes, Marc, Müller-Quade, Jörn 2025 置信度 0.66
• Attack Once, Compromise All? On the Scalability of Attacks
  Electronic voting schemes are often criticized for being insecure, on the grounds that a successful attack would allow an adversary to manipulate all votes at once. It is argued that attacks therefore have a higher impact at lower adversary costs compared to p…
  datacite Hetzel, Eva, Nemes, Marc, Müller-Quade, Jörn 2025 置信度 0.66
• vantage6
  Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains,…
  datacite Martin, Frank, Beusekom, Bart, Leurs, Richard, Sieswerda, Melle 等 2025 置信度 0.66 federated learningmachine learningdata analysissecure multiparty computationprivacy enhancing technology
• vantage6
  Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains,…
  datacite Martin, Frank, Beusekom, Bart, Leurs, Richard, Sieswerda, Melle 等 2025 置信度 0.66 federated learningmachine learningdata analysissecure multiparty computationprivacy enhancing technology
• Incomplete quantum oblivious transfer with perfect one-sided security
  Oblivious transfer is a fundamental cryptographic primitive which is useful for secure multiparty computation. There are several variants of oblivious transfer. We consider 1 out of 2 oblivious transfer, where a sender sends two bits of information to a receiv…
  datacite Reichmuth, David, Puthoor, Ittoop Vergheese, Wallden, Petros, Andersson, Erika 2024 置信度 0.66 Quantum Physics (quant-ph)FOS: Physical sciencesFOS: Physical sciences
• GDI D8.6 - Report on privacy-enhancing solutions
  GDI Pillar III aims to explore use cases and innovative applications for analysing genomic and clinical data, ideally supported by the infrastructure being deployed at the national nodes within Pillar II. As described in the Report on federated learning techno…
  datacite Verachtert, Wilfried 2025 置信度 0.66 European Genomic Data InfrastructureGDI1+ Million Genomes InitiativePrivacy-Enhancing TechnologiesPrivacy-Preserving Technologies
• GDI D8.6 - Report on privacy-enhancing solutions
  GDI Pillar III aims to explore use cases and innovative applications for analysing genomic and clinical data, ideally supported by the infrastructure being deployed at the national nodes within Pillar II. As described in the Report on federated learning techno…
  datacite Verachtert, Wilfried 2025 置信度 0.66 European Genomic Data InfrastructureGDI1+ Million Genomes InitiativePrivacy-Enhancing TechnologiesPrivacy-Preserving Technologies
• Seagull: Privacy preserving network verification system
  The Internet relies on routing protocols to direct traffic efficiently across interconnected networks, with the Border Gateway Protocol (BGP) serving as the core mechanism managing routing between autonomous systems. However, BGP configurations are largely man…
  datacite Daneshamooz, Jaber, Yu, Melody, Maddury, Sucheer 2024 置信度 0.66 Cryptography and Security (cs.CR)Networking and Internet Architecture (cs.NI)FOS: Computer and information sciencesFOS: Computer and information sciences
• Multiparty Computation and Secure Aggregation Approaches for Privacy-Preserving URL-based Phishing Detection
  datacite Akbari Gurabi, Mehdi, Mandal, Avikarsha 2024 置信度 0.66
• vantage6
  Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains,…
  datacite Martin, Frank, Beusekom, Bart, Leurs, Richard, Sieswerda, Melle 等 2025 置信度 0.66 federated learningmachine learningdata analysissecure multiparty computationprivacy enhancing technology
• vantage6
  Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains,…
  datacite Martin, Frank, Beusekom, Bart, Leurs, Richard, Sieswerda, Melle 等 2025 置信度 0.66 federated learningmachine learningdata analysissecure multiparty computationprivacy enhancing technology
• vantage6
  Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains,…
  datacite Martin, Frank, Beusekom, Bart, Leurs, Richard, Sieswerda, Melle 等 2025 置信度 0.66 federated learningmachine learningdata analysissecure multiparty computationprivacy enhancing technology
• Funciones pseudoaleatorias inconscientes en grupos no conmutativos
  Las aplicaciones de las funciones pseudoaleatorias inconscientes en la criptografía y en la seguridad de la información son múltiples. Pueden citarse la derivación de claves basadas en contraseñas, acuerdo de claves basados en contraseñas, password hardening, …
  datacite Ledo Baster, David Ricardo, Martínez Rodríguez, Huber 2025 置信度 0.66 criptografía no conmutativaelemento conjugadorfunciones pseudoaleatorias inconscientesprotocolos criptográficosMSC 20F12
• Funciones pseudoaleatorias inconscientes en grupos no conmutativos
  Las aplicaciones de las funciones pseudoaleatorias inconscientes en la criptografía y en la seguridad de la información son múltiples. Pueden citarse la derivación de claves basadas en contraseñas, acuerdo de claves basados en contraseñas, password hardening, …
  datacite Ledo Baster, David Ricardo, Martínez Rodríguez, Huber 2025 置信度 0.66 criptografía no conmutativaelemento conjugadorfunciones pseudoaleatorias inconscientesprotocolos criptográficosMSC 20F12
• Secure Multi-Key Homomorphic Encryption with Application to Privacy-Preserving Federated Learning
  Multi-Key Homomorphic Encryption (MKHE), proposed by Lopez-Alt et al. (STOC 2012), allows for performing arithmetic computations directly on ciphertexts encrypted under distinct keys. Subsequent works by Chen and Dai et al. (CCS 2019) and Kim and Song et al. (…
  datacite Wu, Jiahui, Sun, Tiecheng, Luo, Fucai, Wang, Haiyan 等 2025 置信度 0.66 Cryptography and Security (cs.CR)FOS: Computer and information sciencesFOS: Computer and information sciences
• vantage6
  Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains,…
  datacite Martin, Frank, Beusekom, Bart, Leurs, Richard, Sieswerda, Melle 等 2025 置信度 0.66 federated learningmachine learningdata analysissecure multiparty computationprivacy enhancing technology
• vantage6
  Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains,…
  datacite Martin, Frank, Beusekom, Bart, Leurs, Richard, Sieswerda, Melle 等 2025 置信度 0.66 federated learningmachine learningdata analysissecure multiparty computationprivacy enhancing technology
• High-Throughput Secure Multiparty Computation with an Honest Majority in Various Network Settings
  datacite Harth-Kitzerow, Christopher, Ajith Suresh, Yonqing Wang, Yalame, Hossein 等 2024 置信度 0.66
• vantage6
  Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains,…
  datacite Martin, Frank, Beusekom, Bart, Leurs, Richard, Sieswerda, Melle 等 2025 置信度 0.66 federated learningmachine learningdata analysissecure multiparty computationprivacy enhancing technology
• Towards a Local Electricity Trading Market based on Secure Multiparty Computation
  datacite Aysajan Abidin, Abdelrahaman Aly, Cleemput, Sara, Mustafa, Mustafa A 2016 置信度 0.66
• PUMA: Secure Inference of LLaMA-7B in Five Minutes
  With ChatGPT as a representative, tons of companies have began to provide services based on large Transformers models. However, using such a service inevitably leak users' prompts to the model provider. Previous studies have studied secure inference for Transf…
  datacite Dong, Ye, Lu, Wen-jie, Zheng, Yancheng, Wu, Haoqi 等 2023 置信度 0.66 Cryptography and Security (cs.CR)FOS: Computer and information sciencesFOS: Computer and information sciences
• vantage6
  Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains,…
  datacite Martin, Frank, Beusekom, Bart, Leurs, Richard, Sieswerda, Melle 等 2025 置信度 0.66 federated learningmachine learningdata analysissecure multiparty computationprivacy enhancing technology
• PETs and AI: Privacy Washing and the Need for a PETs Evaluation Framework (Dagstuhl Seminar 25112)
  As public awareness of data collection practices and regulatory frameworks grows, privacy-enhancing technologies (PETs) have emerged as a promising approach to reconciling data utility with individual privacy rights. PETs underpin privacy-preserving machine le…
  datacite De Cristofaro, Emiliano, Shrishak, Kris, Strufe, Thorsten, Troncoso, Carmela 等 2025 置信度 0.66 Privacy Enhancing Technologies (PET)Privacy EvaluationPrivacy HarmPrivacy ThreatsPrivacy Washing
• Utilizing Model-Free Reinforcement Learning for Optimizing Secure Multi-Party Computation Protocols
  In this manuscript, we explore the application of model-free reinforcement learning in optimizing secure multiparty computation (SMPC) protocols. SMPC is a crucial tool for performing computations on private data without the need to disclose it, holding signif…
  datacite Sayyadi, Javad, Nangir, Mahdi, Feghhi, Mahmood Mohassel, Sayyadi, Hamid 2025 置信度 0.66 Signal Processing (eess.SP)FOS: Electrical engineering, electronic engineering, information engineeringFOS: Electrical engineering, electronic engineering, information engineering
• Secure and Efficient Multiparty Computation on Genomic Data
  datacite Md. Momin Al Aziz 2016 置信度 0.66
• vantage6
  Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains,…
  datacite Martin, Frank, Beusekom, Bart, Leurs, Richard, Sieswerda, Melle 等 2025 置信度 0.66 federated learningmachine learningdata analysissecure multiparty computationprivacy enhancing technology
• Cheat-Penalised Quantum Weak Coin-Flipping
  Coin-flipping is a fundamental task in two-party cryptography where two remote mistrustful parties wish to generate a shared uniformly random bit. While quantum protocols promising near-perfect security exist for weak coin-flipping -- when the parties want opp…
  datacite Arora, Atul Singh, Miller, Carl A., Morales, Mauro E. S., Sikora, Jamie 2025 置信度 0.66 Quantum Physics (quant-ph)Cryptography and Security (cs.CR)FOS: Physical sciencesFOS: Physical sciencesFOS: Computer and information sciences
• Implementing Key Partitioning for Secure Multiparty Computation
  datacite Pandey, Ravi Krishan 2013 置信度 0.66
• Accelerating Multiparty Noise Generation Using Lookups
  There is growing interest in combining Differential Privacy (DP) and Secure Multiparty Computation (MPC) to protect distributed database queries from both computational parties and those observing the result. This requires implementing both query evaluation an…
  datacite Meisingseth, Fredrik, Rechberger, Christian, Schmid, Fabian 2025 置信度 0.66
• Accelerating Multiparty Noise Generation Using Lookups
  There is growing interest in combining Differential Privacy (DP) and Secure Multiparty Computation (MPC) to protect distributed database queries from both computational parties and those observing the result. This requires implementing both query evaluation an…
  datacite Meisingseth, Fredrik, Rechberger, Christian, Schmid, Fabian 2025 置信度 0.66
• Implementing federated learning with privacy-preserving encryption to secure patient-derived imaging and sequencing data from cyber intrusions
  The growing adoption of artificial intelligence (AI) and data-driven analytics in healthcare has accelerated the integration of large-scale patient-derived imaging and genomic sequencing data into clinical workflows. However, this surge in biomedical data shar…
  datacite Kalejaiye, Adebayo Nurudeen, Shallom, Kigbu, Chukwuani, Elvis Nnaemeka 2025 置信度 0.66 Federated LearningPrivacy-Preserving EncryptionMedical ImagingGenomic Data SecurityHomomorphic Encryption
• Implementing federated learning with privacy-preserving encryption to secure patient-derived imaging and sequencing data from cyber intrusions
  The growing adoption of artificial intelligence (AI) and data-driven analytics in healthcare has accelerated the integration of large-scale patient-derived imaging and genomic sequencing data into clinical workflows. However, this surge in biomedical data shar…
  datacite Kalejaiye, Adebayo Nurudeen, Shallom, Kigbu, Chukwuani, Elvis Nnaemeka 2025 置信度 0.66 Federated LearningPrivacy-Preserving EncryptionMedical ImagingGenomic Data SecurityHomomorphic Encryption
• vantage6
  Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains,…
  datacite Martin, Frank, Beusekom, Bart, Leurs, Richard, Sieswerda, Melle 等 2025 置信度 0.66 federated learningmachine learningdata analysissecure multiparty computationprivacy enhancing technology
• vantage6
  Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains,…
  datacite Martin, Frank, Beusekom, Bart, Leurs, Richard, Sieswerda, Melle 等 2025 置信度 0.66 federated learningmachine learningdata analysissecure multiparty computationprivacy enhancing technology
• vantage6
  Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains,…
  datacite Martin, Frank, Beusekom, Bart, Leurs, Richard, Sieswerda, Melle 等 2025 置信度 0.66 federated learningmachine learningdata analysissecure multiparty computationprivacy enhancing technology
• EasySMPC: a simple but powerful no-code tool for practical secure multiparty computation
  Background: Modern biomedical research is data-driven and relies heavily on the re-use and sharing of data. Biomedical data, however, is subject to strict data protection requirements. Due to the complexity of the data required and the scale of data use, obtai…
  datacite Wirth, Felix Nikolaus, Kussel, Tobias, Müller, Armin, Hamacher, Kay 等 2025 置信度 0.66
• vantage6
  Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains,…
  datacite Martin, Frank, Beusekom, Bart, Leurs, Richard, Sieswerda, Melle 等 2025 置信度 0.66 federated learningmachine learningdata analysissecure multiparty computationprivacy enhancing technology
• vantage6
  Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains,…
  datacite Martin, Frank, Beusekom, Bart, Leurs, Richard, Sieswerda, Melle 等 2025 置信度 0.66 federated learningmachine learningdata analysissecure multiparty computationprivacy enhancing technology
• Secure Multiparty Computation via Fully Homomorphic Encryption Scheme
  crossref Jing-Li Han, Zhao-Li Wang, Ya-Qing Shi, Mei-Juan Wang 等 2020-03-27T05:25:22Z 置信度 0.70
• Efficient secure multiparty computation of sparse vector dot products
  crossref Michael J. Collins 2018-09-06T04:25:06Z 置信度 0.70
• A secure multiparty micropayment protocol for internet access over WLAN mesh networks
  <p>Presently, multi-hop WLAN mesh networks have become an alternative to wired networks for last-mile user access enabling numerous internet-based services. Thus, we have proposed MMPay, a secure multiparty micropayment protocol for internet access over …
  crossref Nitish Biswas 2021-05-22T13:57:46Z 置信度 0.70
• Non-Interactive Secure Multiparty Computation
  crossref Amos Beimel, Ariel Gabizon, Yuval Ishai, Eyal Kushilevitz 等 2014-07-14T04:27:09Z 置信度 0.70
• A Privacy-Preserving Protocol for Secure Marine Data Sharing: Integrating Zero-Knowledge Proofs and Secure Multiparty Computation in Blockchain
  crossref Ankit Kumar, Jong Hyuk Park 2025-05-14T13:51:20Z 置信度 0.70
• Secure Multiparty Computation of DNF
  crossref Kun Peng 2007-11-16T15:10:22Z 置信度 0.70
• Using Sharemind as a Tool to Develop an Internet Voting System with Secure Multiparty Computation
  crossref Ruahden Dang-awan, Joyce Anne Piscos, Richard Bryann Chua 2019-02-05T02:33:11Z 置信度 0.70
• Secure Multiparty Computation: From Millionaires Problem to Anonymizer
  crossref Rashid Sheikh, Durgesh Kumar Mishra, Beerendra Kumar 2011-02-10T13:55:52Z 置信度 0.70
• Privacy Preserving OLAP over Distributed XML Data: A Theoretically-Sound Secure-Multiparty-Computation Approach
  crossref Alfredo Cuzzocrea, Elisa Bertino 2011-03-08T03:59:46Z 置信度 0.70
• Privacy-Preserving Deep Federated Learning on the Edge Using Homomorphic Encryption and Secure Multiparty Computation
  crossref Noman Aasif Gudur, Mohamed El-Dosuky, Sherif Kamel 2025-12-29T08:59:06Z 置信度 0.70
• General quantum secure multiparty computation protocol for simultaneous summation and multiplication
  Abstract Quantum secure multiparty computation occupies an important place in quantum cryptography. Based on access structure and linear secret sharing, we propose a new general quantum secure multiparty computation protocol for simultaneous summation and mult…
  crossref Fulin Li, Mei Luo, Shixin Zhu, Binbin Pang 2023-12-05T17:42:20Z 置信度 0.70
• Comparative Evaluation of Elliptic Curve Cryptography Based Homomorphic Encryption Schemes for a Novel Secure Multiparty Computation
  crossref Sankita J. Patel, Ankit Chouhan, Devesh C. Jinwala 2014-01-17T02:13:13Z 置信度 0.70
• On Combining Privacy with Guaranteed Output Delivery in Secure Multiparty Computation
  crossref Yuval Ishai, Eyal Kushilevitz, Yehuda Lindell, Erez Petrank 2006-09-23T06:21:52Z 置信度 0.70
• Leveraging Secure Multiparty Computation in the Internet of Things
  crossref Marcel von Maltitz, Georg Carle 2018-07-06T12:36:06Z 置信度 0.70
• AI-Powered Blockchain - A Decentralized Secure Multiparty Computation Protocol for IoV
  crossref Gunasekaran Raja, Yelisetty Manaswini, Gaayathri Devi Vivekanandan, Harish Sampath 等 2020-08-10T21:55:09Z 置信度 0.70
• Global-Scale Secure Multiparty Computation
  crossref Xiao Wang, Samuel Ranellucci, Jonathan Katz 2017-10-27T12:48:18Z 置信度 0.70
• Secure Multiparty Computation for Privacy‐Preserving Machine Learning in Healthcare: A Comprehensive Survey
  ABSTRACT Privacy‐preserving machine learning (PPML) using secure multiparty computation (SMC) is emerging as a promising approach for enabling collaborative data analysis in healthcare while protecting sensitive patient information. This survey provides a comp…
  crossref Vankamamidi S. Naresh, A. Venkata Raju, O. Srinivasa Rao 2025-09-23T01:39:28Z 置信度 0.70
• Supporting private data on Hyperledger Fabric with secure multiparty computation
  crossref F. Benhamouda, S. Halevi, T. Halevi 2019-04-27T01:07:26Z 置信度 0.70
• Improved Secure Multiparty Computation with a Dishonest Majority via Quantum Means
  crossref Yan-Bing Li, Qiao-Yan Wen, Su-Juan Qin 2012-08-28T09:03:38Z 置信度 0.70
• Multiparty Computation
  crossref Berry Schoenmakers 2006-09-09T15:07:16Z 置信度 0.70
• A secure multiparty micropayment protocol for internet access over WLAN mesh networks
  <p>Presently, multi-hop WLAN mesh networks have become an alternative to wired networks for last-mile user access enabling numerous internet-based services. Thus, we have proposed MMPay, a secure multiparty micropayment protocol for internet access over …
  crossref Nitish Biswas 2021-05-22T13:57:47Z 置信度 0.70
• Secure Multiparty Computation over Elliptic Curve Cryptography
  This study proposes a secure mobile voting system that integrates elliptic curve cryptography (ECC) with secure multiparty computation (SMPC) to guarantee vote confidentiality, integrity, and verifiability. Designed to enable scalable, privacy-preserving elect…
  crossref Domven L., A. D. Hina, A. M Kwami, C. M. Miri 等 2025-09-05T02:10:20Z 置信度 0.70
• Feature Selection Method Based on Secure MultiParty Computation and Its Application in State Evaluation
  crossref Binyu Xie, Hao Shi, Zehui Zhang, Yibo Zhu 等 2026-07-02T19:41:35Z 置信度 0.70
• A Privacy-Preserving Log-Rank Test for the Kaplan-Meier Estimator With Secure Multiparty Computation: Algorithm Development and Validation (Preprint)
  BACKGROUND Patient data is considered particularly sensitive personal data. Privacy regulations strictly govern the use of patient data and restrict their exchange. However, medical research can benefit from multicentric studies in which patient data from diff…
  crossref Marcel von Maltitz, Hendrik Ballhausen, David Kaul, Daniel F Fleischmann 等 2021-01-18T14:01:23Z 置信度 0.70
• $${\varvec{1/p}}$$-Secure Multiparty Computation without an Honest Majority and the Best of Both Worlds
  crossref Amos Beimel, Yehuda Lindell, Eran Omri, Ilan Orlov 2020-07-16T16:03:43Z 置信度 0.70
• Secure Multiparty Computation of Principal Component Analysis
  crossref Sang-Pil Kim, Sanghun Lee, Myeong-Seon Gil, Yang-Sae Moon 等 2015-08-11T20:44:33Z 置信度 0.70
• Fast Privacy-Preserving Text Classification Based on Secure Multiparty Computation
  crossref Amanda Resende, Davis Railsback, Rafael Dowsley, Anderson C. A. Nascimento 等 2022-01-18T17:01:24Z 置信度 0.70
• Poster: A Secure Multiparty Computation Platform for Squeaky-Clean Data Rooms
  crossref Pankaj Dayama, Vinayaka Pandit, Sikhar Patranabis, Abhishek Singh 等 2024-12-09T12:19:20Z 置信度 0.70
• Brief announcement
  crossref Varsha Dani, Valerie King, Mahnush Movahedi, Jared Saia 2012-07-19T14:38:27Z 置信度 0.70
• An Entropy-View Secure Multiparty Computation Protocol Based on Semi-Honest Model
  Data interaction scenarios involving multiple parties in network communities have problems of trust, data security, and reliability of the parties, and secure multiparty computation(SMPC) can effectively solve these problems. To address the security and fairne…
  crossref Yun Luo, Yuling Chen, Tao Li, Yilei Wang 等 2022-07-14T12:41:35Z 置信度 0.70