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• 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 2026 置信度 0.66 federated learningmachine learningdata analysissecure multiparty computationprivacy enhancing technology
• Federated Generation of Synthetic RNA-seq Data
  Access to genomic data is highly regulated due to its sensitive nature. While safeguards are essential, cumbersome data access processes pose a significant barrier to the development of AI methods for genomics. Synthetic data generation can mitigate this tensi…
  datacite Filienko, Daniil, De Cock, Martine, Pentyala, Sikha 2026 置信度 0.66 Cryptography and Security (cs.CR)FOS: Computer and information sciencesFOS: Computer and information sciences
• UVCC Phase 6 Native Parallelism: Private, Verifiable, Fully-Parallel GPU Computation across Untrusted Domains
  We present UVCC (Universal Verifiable Confidential Computing), a practical system for private and verifiable GPU computation across mutually distrustful administrative domains. UVCC achieves (i) confidentiality for client secrets using three-party replicated s…
  datacite Gairola, N 2025 置信度 0.66 confidential computingsecure multiparty computationverifiable computationGPUreplicated secret sharing
• UVCC Phase 6 Native Parallelism: Private, Verifiable, Fully-Parallel GPU Computation across Untrusted Domains
  We present UVCC (Universal Verifiable Confidential Computing), a practical system for private and verifiable GPU computation across mutually distrustful administrative domains. UVCC achieves (i) confidentiality for client secrets using three-party replicated s…
  datacite Gairola, N 2025 置信度 0.66 confidential computingsecure multiparty computationverifiable computationGPUreplicated secret sharing
• Fast Bounded-Independence Functions and Their Duals
  We continue the study of {\em fast} functions, computable by linear-size circuits, that share useful properties of random functions. Motivated by cryptographic applications, we generalize and improve on previous results in this area, obtaining the following re…
  datacite Brehm, Martijn, Ishai, Yuval, Resch, Nicolas 2026 置信度 0.66 Cryptography and Security (cs.CR)Information Theory (cs.IT)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 2026 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 2026 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 2026 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 2026 置信度 0.66 federated learningmachine learningdata analysissecure multiparty computationprivacy enhancing technology
• SECURE MULTIPARTY COMPUTATION FOR GEOMETRIC PROBLEMS
  datacite Tiwari, Mukesh, Gokul Kc 2024 置信度 0.66
• Improving Scalability, Privacy, and Decentralization of Blockchains and their Applications via Multiparty Computation
  Since the advent of Bitcoin in 2008, a myriad of blockchain systems have emerged. Blockchains provide decentralized systems aiming to remove any trust in centralized parties. While Bitcoin provides simple money transfer and rudimentary scripting capabilities, …
  datacite Schlosser, Benjamin 2024 置信度 0.66 004
• 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 2026 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 2026 置信度 0.66 federated learningmachine learningdata analysissecure multiparty computationprivacy enhancing technology
• Multilaterally Secure Pervasive Cooperation
  People tend to interact and communicate with others throughout their life. In the age of pervasive computing, information and communication technology (ICT) that is no longer bound to desktop computers enables digital cooperations in everyday life and work in …
  datacite Weber, Stefan G. 2011 置信度 0.66 004
• Secure Computations on Non-Integer Values
  Die Forschung zu Secure Multiparty Computation (SMC) begann im Jahr 1982, als Andrew C. Yao das Millionärsproblem vorstellte. Seitdem hat die Wissenschaft in diesem Bereich große Fortschritte gemacht, viele sicherheitskritische Anwendungen wurden mittels SMC r…
  datacite Franz, Martin 2012 置信度 0.66 004
• 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 2026 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 2026 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 2026 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 2026 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 2026 置信度 0.66 federated learningmachine learningdata analysissecure multiparty computationprivacy enhancing technology
• Enhancing Privacy In Federated Learning: A Comprehensive Survey Of Preservation Techniques
  Federated Learning (FL) enables multiple devices or organizations to collaboratively train machine learning models without sharing raw data, thus improving privacy. However, FL is vulnerable to privacy threats like model inversion, membership inference, and da…
  datacite D Naga Bharghavi, M Deepthi, K Manga Devi, M Aswitha 等 2026 置信度 0.66
• Enhancing Privacy In Federated Learning: A Comprehensive Survey Of Preservation Techniques
  Federated Learning (FL) enables multiple devices or organizations to collaboratively train machine learning models without sharing raw data, thus improving privacy. However, FL is vulnerable to privacy threats like model inversion, membership inference, and da…
  datacite D Naga Bharghavi, M Deepthi, K Manga Devi, M Aswitha 等 2026 置信度 0.66
• Coding-theoretic approaches to distributed caching, storage and computing
  The next-generation communication networks aim to bring data storage and computation closer to the end users and Internet of things (IoT) endpoints by exploiting edge servers and/or memory available at edge devices. This greatly improves the efficiency and eco…
  datacite Mital, Nitish 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 2026 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 2026 置信度 0.66 federated learningmachine learningdata analysissecure multiparty computationprivacy enhancing technology
• Cryptalphabet Soup: DPFs meet MPC and ZKPs
  Secure multiparty computation (MPC) protocols enable multiple parties to collaborate on a computation using private inputs possessed by the different parties in the computation. At the same time, MPC protocols ensure that no participating party learns anything…
  datacite Storrier, Kyle 2023 置信度 0.66 CryptographySecure Multiparty ComputationMPCZero-Knowledge ProofZKP
• 6th International Conference on Big Data, IoT and Machine Learning (BIOM 2026)
  6th International Conference on Big Data, IoT and Machine Learning (BIOM 2026) May 23 ~ 24, 2026, Vancouver, Canada https://crbl2026.org/biom/index Scope 6th International Conference on Big Data, IoT and Machine Learning (BIOM 2026) serves as a premier global …
  datacite Yaacoub, Aya 2026 置信度 0.66
• 6th International Conference on Big Data, IoT and Machine Learning (BIOM 2026)
  6th International Conference on Big Data, IoT and Machine Learning (BIOM 2026) May 23 ~ 24, 2026, Vancouver, Canada https://crbl2026.org/biom/index Scope 6th International Conference on Big Data, IoT and Machine Learning (BIOM 2026) serves as a premier global …
  datacite Yaacoub, Aya 2026 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 2026 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 2026 置信度 0.66 federated learningmachine learningdata analysissecure multiparty computationprivacy enhancing technology
• Beyond Latency: A System-Level Characterization of MPC and FHE for PPML
  Privacy protection has become an increasing concern in modern machine learning applications. Privacy-preserving machine learning (PPML) has attracted growing research attention, with approaches such as secure multiparty computation (MPC) and fully homomorphic …
  datacite Huang, Pengzhi, Maeng, Kiwan, Suh, G. Edward 2026 置信度 0.66 Cryptography and Security (cs.CR)FOS: Computer and information sciences
• Secure and Privacy-Preserving Vertical Federated Learning
  We propose a novel end-to-end privacy-preserving framework, instantiated by three efficient protocols for different deployment scenarios, covering both input and output privacy, for the vertically split scenario in federated learning (FL), where features are s…
  datacite Jin, Shan, Rachuri, Sai Rahul, Wang, Yizhen, Nascimento, Anderson C. A. 等 2026 置信度 0.66 Cryptography and Security (cs.CR)Artificial Intelligence (cs.AI)Distributed, Parallel, and Cluster Computing (cs.DC)FOS: Computer and information sciencesFOS: Computer and information sciences
• A Survey on the Applications of Zero-Knowledge Proofs
  Zero-knowledge proofs (ZKPs) enable computational integrity and privacy by allowing one party to prove the truth of a statement without revealing underlying data. Compared with alternatives such as homomorphic encryption and secure multiparty computation, ZKPs…
  datacite Lavin, Ryan, Liu, Xuekai, Mohanty, Hardhik, Norman, Logan 等 2024 置信度 0.66 Cryptography and Security (cs.CR)Computational Complexity (cs.CC)FOS: Computer and information sciencesFOS: Computer and information sciences
• EncFormer: Secure and Efficient Transformer Inference over Encrypted Data
  Transformer inference in machine-learning-as-a-service (MLaaS) raises privacy concerns for sensitive user inputs. Prior secure solutions that combine fully homomorphic encryption (FHE) and secure multiparty computation (MPC) are bottlenecked by inefficient FHE…
  datacite Zhu, Yufan, Jin, Chao, Aung, Khin Mi Mi, Xiao, Xiaokui 2026 置信度 0.66 Cryptography and Security (cs.CR)FOS: Computer and information sciencesFOS: Computer and information sciencesC.2.4; I.2.7; E.3
• Privacy-Preserving Machine Learning for IoT: A Cross-Paradigm Survey and Future Roadmap
  The rapid proliferation of the Internet of Things has intensified demand for robust privacy-preserving machine learning mechanisms to safeguard sensitive data generated by large-scale, heterogeneous, and resource-constrained devices. Unlike centralized environ…
  datacite Zaman, Zakia, Gauravaram, Praveen, Hassan, Mahbub, Jha, Sanjay 等 2026 置信度 0.66 Machine Learning (cs.LG)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 2026 置信度 0.66 federated learningmachine learningdata analysissecure multiparty computationprivacy enhancing technology
• On Black-Box Complexity and Adaptive, Universal Composability of Cryptographic Tasks
  Two main goals of modern cryptography are to identify the minimal assumptions necessary to construct secure cryptographic primitives as well as to construct secure protocols in strong and realistic adversarial models. In this thesis, we address both of these f…
  datacite Dachman-Soled, Dana 2011 置信度 0.66 Computer science
• 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 2026 置信度 0.66 federated learningmachine learningdata analysissecure multiparty computationprivacy enhancing technology
• Janus: Safe Biometric Deduplication for Humanitarian Aid Distribution
  Humanitarian organizations provide aid to people in need. To use their limited budget efficiently, their distribution processes must ensure that legitimate recipients cannot receive more aid than they are entitled to. Thus, it is essential that recipients can …
  datacite EdalatNejad, Kasra, Lueks, Wouter, Justinas, Sukaitis, Graf Narbel, Vincent 等 2024 置信度 0.66
• Janus: Safe Biometric Deduplication for Humanitarian Aid Distribution
  Humanitarian organizations provide aid to people in need. To use their limited budget efficiently, their distribution processes must ensure that legitimate recipients cannot receive more aid than they are entitled to. Thus, it is essential that recipients can …
  datacite EdalatNejad, Kasra, Lueks, Wouter, Justinas, Sukaitis, Graf Narbel, Vincent 等 2024 置信度 0.66
• Gossiping for Communication-Efficient Broadcast
  Byzantine Broadcast is crucial for many cryptographic pro- tocols such as secret sharing, multiparty computation and blockchain consensus. In this paper we apply gossiping (propagating a message by sending to a few random parties who in turn do the same, until…
  datacite Tsimos, Georgios, Loss, Julian, Papamanthou, Charalampos 2023 置信度 0.66
• Gossiping for Communication-Efficient Broadcast
  Byzantine Broadcast is crucial for many cryptographic pro- tocols such as secret sharing, multiparty computation and blockchain consensus. In this paper we apply gossiping (propagating a message by sending to a few random parties who in turn do the same, until…
  datacite Tsimos, Georgios, Loss, Julian, Papamanthou, Charalampos 2023 置信度 0.66
• Constant Ciphertext-Rate Non-committing Encryption from Standard Assumptions
  Non-committing encryption (NCE) is a type of public key encryption which comes with the ability to equivocate ciphertexts to encryptions of arbitrary messages, i.e., it allows one to find coins for key generation and encryption which “explain” a given cipherte…
  datacite Brakerski, Zvika, Branco, Pedro, Döttling, Nico, Garg, Sanjam 等 2023 置信度 0.66
• Constant Ciphertext-Rate Non-committing Encryption from Standard Assumptions
  Non-committing encryption (NCE) is a type of public key encryption which comes with the ability to equivocate ciphertexts to encryptions of arbitrary messages, i.e., it allows one to find coins for key generation and encryption which “explain” a given cipherte…
  datacite Brakerski, Zvika, Branco, Pedro, Döttling, Nico, Garg, Sanjam 等 2023 置信度 0.66
• Efficient UC Commitment Extension with Homomorphism for Free (and Applications)
  Homomorphic universally composable (UC) commitments allow for the sender to reveal the result of additions and multiplications of values contained in commitments without revealing the values themselves while assuring the receiver of the correctness of such com…
  datacite Cascudo, Ignacio, Damgård, Ivan, David, Bernardo, Döttling, Nico 等 2023 置信度 0.66
• Efficient UC Commitment Extension with Homomorphism for Free (and Applications)
  Homomorphic universally composable (UC) commitments allow for the sender to reveal the result of additions and multiplications of values contained in commitments without revealing the values themselves while assuring the receiver of the correctness of such com…
  datacite Cascudo, Ignacio, Damgård, Ivan, David, Bernardo, Döttling, Nico 等 2023 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 2026 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 2026 置信度 0.66 federated learningmachine learningdata analysissecure multiparty computationprivacy enhancing technology
• Efficient Multiparty Computation from Lossy Threshold Encryption
  This dissertation includes four contributions concerning secure multiparty computation. The first contribution is a new lossy threshold encryption scheme. This is the first encryption scheme that is both a lossy and a threshold encryption scheme. The second co…
  datacite Nargis, Isheeta 2019 置信度 0.66 SecurityCryptographyMultiparty ComputationEncryptionComputer Science
• A (2 + 1)-Party One-Instruction Set Processor for Private Function Evaluation
  Secure multiparty computation (MPC) is a powerful cryptographic technique which allows mutually distrusting parties to compute a function on their shared inputs. MPC can be viewed as encompassing two main categories, secure function evaluation (SFE) where the …
  datacite Jiang, Christopher 2025 置信度 0.66 CryptographySecure Multiparty ComputationPrivate Function EvaluationGarbled ProcessorComputer Science
• Language-Based Techniques for Policy-Agnostic Oblivious Computation
  Protecting personal information is growing increasingly important to the general public, to the point that major tech companies now advertise the privacy features of their products. Despite this, it remains challenging to implement applications that do not lea…
  datacite Ye, Qianchuan 2024 置信度 0.66 Programming languagesData and information privacyData security and protection
• Language-Based Techniques for Policy-Agnostic Oblivious Computation
  Protecting personal information is growing increasingly important to the general public, to the point that major tech companies now advertise the privacy features of their products. Despite this, it remains challenging to implement applications that do not lea…
  datacite Ye, Qianchuan 2024 置信度 0.66 Programming languagesData and information privacyData security and protection
• Efficient Building Blocks for Secure Multiparty Computation and Their Applications
  Secure multi-party computation (MPC) enables mutually distrusting parties to compute securely over their private data. It is a natural approach for building distributed applications with strong privacy guarantees, and it has been used in more and more real-wor…
  datacite Lu, Donghang 2022 置信度 0.66 Cryptography
• Efficient Building Blocks for Secure Multiparty Computation and Their Applications
  Secure multi-party computation (MPC) enables mutually distrusting parties to compute securely over their private data. It is a natural approach for building distributed applications with strong privacy guarantees, and it has been used in more and more real-wor…
  datacite Lu, Donghang 2022 置信度 0.66 Cryptography
• Efficient and Secure Equality-based Two-party Computation
  Multiparty computation refers to a scenario in which multiple distinct yet connected parties aim to jointly compute a functionality. Over recent decades, with the rapid spread of the internet and digital technologies, multiparty computation has become an incre…
  datacite Darivandpour, Javad 2021 置信度 0.66 Applied computing not elsewhere classifiedTheory of computation not elsewhere classifiedSystem and network security
• Efficient and Secure Equality-based Two-party Computation
  Multiparty computation refers to a scenario in which multiple distinct yet connected parties aim to jointly compute a functionality. Over recent decades, with the rapid spread of the internet and digital technologies, multiparty computation has become an incre…
  datacite Darivandpour, Javad 2021 置信度 0.66 Applied computing not elsewhere classifiedTheory of computation not elsewhere classifiedSystem and network security
• Enhanced Rényi Entropy-Based Post-Quantum Key Agreement with Provable Security and Information-Theoretic Guarantees
  This paper presents an enhanced post-quantum key agreement protocol based on Rényi entropy, addressing vulnerabilities in the original construction while preserving information-theoretic security properties. We develop a theoretical framework leveraging entrop…
  datacite Xu, Ruopengyu, Liu, Chenglian 2025 置信度 0.66 Cryptography and Security (cs.CR)Information Theory (cs.IT)Quantum Physics (quant-ph)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 2026 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 2026 置信度 0.66 federated learningmachine learningdata analysissecure multiparty computationprivacy enhancing technology
• Secure Multiparty Computation Based Privacy Preserving Smart Metering System
  Smart metering systems provide high resolution, realtime end user power consumption data for utilities to better monitor and control the system, and for end users to better manage their energy usage and bills. However, the high resolution realtime power consum…
  datacite Thoma, Cory, Cui, Tao, Franchetti, Franz 2012 置信度 0.66 Digital processor architecturesOther information and computing sciences not elsewhere classifiedElectrical engineering not elsewhere classified
• TFPA: A traceable federated privacy aggregation protocol
  preprints 2022 置信度 0.74
• FeverIQ - A Privacy-Preserving COVID-19 Symptom Tracker with 3.6 Million Reports
  preprints 2020 置信度 0.74
• FL-QSAR: a federated learning based QSAR prototype for collaborative drug discovery
  preprints 2020 置信度 0.74
• Revealing the causative variant in Mendelian patient genomes without revealing patient genomes
  preprints 2017 置信度 0.74
• Medical records condensation: a roadmap towards healthcare data democratisation
  preprints 2024 置信度 0.74
• Information security - Secure multiparty computation
  crossref 2024-03-13T21:30:36Z 置信度 0.70
• Information security - Secure multiparty computation
  crossref 2024-03-13T21:30:36Z 置信度 0.70
• Secure Multiparty Computation
  crossref 2025-01-10T20:20:57Z 置信度 0.70
• Secure Multiparty Computation Methods
  crossref Murat Kantarcolu, Jaideep Vaidya 2017-10-10T03:35:37Z 置信度 0.70
• Secure Multiparty Computation Methods
  crossref Murat Kantarcolu, Jaideep Vaidya 2018-12-06T08:25:47Z 置信度 0.70
• Secure Comparisons of Single Nucleotide Polymorphisms Using Secure Multiparty Computation: Method Development (Preprint)
  BACKGROUND While genomic variations can provide valuable information for health care 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…
  crossref Andrew Woods, Skyler T Kramer, Dong Xu, Wei Jiang 2022-12-09T17:55:59Z 置信度 0.70
• Secure multiparty computation
  MPC has moved from theoretical study to real-world usage. How is it doing?
  crossref Yehuda Lindell 2020-12-17T18:39:35Z 置信度 0.70
• Ownership Proofs of Digital Works Based on Secure Multiparty Computation
  crossref Yan ZHU 2006-01-11T02:56:56Z 置信度 0.70
• Multiparty Secure Delegated Quantum Computation
  crossref Shuquan Ma, Xuchao Liu, Huagui Li, Heliang Song 2024-05-16T17:22:10Z 置信度 0.70
• Protecting Confidential Data through Non-Statistical Methods
  crossref Lars Vilhuber 2024-08-23T14:36:41Z 置信度 0.70
• A zero-hacking protocol for secure multiparty computation using multiple TTP
  crossref Durgesh Kumar Mishra, Manohar Chandwani 2009-01-29T21:52:21Z 置信度 0.70
• Privacy Implications of Practical Model Design Choices
  crossref Audra McMillan 2024-08-23T14:36:41Z 置信度 0.70
• Introduction
  crossref Jörg Drechsler, Daniel Kifer, Jerome Reiter, Aleksandra Slavković 2024-08-23T14:36:41Z 置信度 0.70
• Cryptographically Secure Multiparty Computation and Distributed Auctions Using Homomorphic Encryption
  We introduce a robust framework that allows for cryptographically secure multiparty computations, such as distributed private value auctions. The security is guaranteed by two-sided authentication of all network connections, homomorphically encrypted bids, and…
  crossref Anunay Kulshrestha, Akshay Rampuria, Matthew Denton, Ashwin Sreenivas 2017-12-12T13:35:00Z 置信度 0.70
• Systems Issues in Formally Private Systems
  crossref Philip Leclerc, Pavel Zhuravlev 2024-08-23T14:36:41Z 置信度 0.70
• Secure Federated Learning Integrated Statistical Modeling for Healthcare Data
  crossref Xiaoqian Jiang, Jihoon Kim, Tsung-Ting Kuo, Lucila Ohno-Machado 2024-08-23T14:36:41Z 置信度 0.70
• A Secure Protocol for Indian Healthcare Sector during Multiparty Computation
  crossref Zulfa Shaikh 2013-01-10T15:52:32Z 置信度 0.70
• Consideration of the XOR-operation Based Secure Multiparty Computation
  crossref Yuji Suga 2015-10-01T18:02:23Z 置信度 0.70
• Multiparty Computation
  crossref 2011-12-31T20:16:01Z 置信度 0.70
• Supporting Private Data on Hyperledger Fabric with Secure Multiparty Computation
  crossref Fabrice Benhamouda, Shai Halevi, Tzipora Halevi 2018-05-17T22:40:06Z 置信度 0.70
• General Composition and Universal Composability in Secure Multiparty Computation
  crossref Yehuda Lindell 2008-03-28T18:49:05Z 置信度 0.70
• Methods for Synthetic Data Generation
  crossref Joshua Snoke, Satkartar K. Kinney 2024-08-23T14:36:41Z 置信度 0.70
• Secure Quantum Protocol for Multiparty Computation
  crossref Amitesh Kumar Pandit, Kakali Chatterjee 2021-03-01T22:46:34Z 置信度 0.70
• Secure Multiparty Computation via Homomorphic Encryption Library
  crossref Sahar M. Ghanem, Islam A. Moursy 2020-03-13T04:41:03Z 置信度 0.70
• Secure Multiparty Computation in Heterogeneous Environment: Survey and Challenges
  Abstract: The sharing of personal data in a distributed environment raises individual privacy concerns. However, analysis of these data may solve various real-life problems, including analysis of medical data, e-auction, secure voting, etc. Such analysis requi…
  crossref Amitesh Kumar Pandit, Kakali Chatterjee 2025-04-18T05:28:46Z 置信度 0.70
• Privacy preserving machine learning using secure multiparty computation
  crossref Suhel Sayyad, Dinesh Kulkarni 2023-06-24T15:25:06Z 置信度 0.70
• Efficient optimisation framework for convolutional neural networks with secure multiparty computation
  crossref Cate Berry, Nikos Komninos 2022-03-04T19:50:49Z 置信度 0.70
• Notice of Violation of IEEE Publication Principles - Distributed changing neighbors k-secure sum protocol for secure multiparty computation
  crossref First A. Neha Pathak, Second B. Shweta Pandey 2014-04-15T17:15:32Z 置信度 0.70
• A Secure Multiparty Computation Solution to Healthcare Frauds and Abuses
  crossref Priyanka Jangde, Durgesh Kumar Mishra 2011-03-15T17:17:17Z 置信度 0.70
• Universally Verifiable Outsourcing and Application to Linear Programming
  In this chapter, we show how to guarantee correctness when applying multiparty computation in outsourcing scenarios. Specifically, we consider how to guarantee the correctness of the result when neither the parties supplying the input nor the parties performin…
  crossref de Hoogh Sebastiaan, Schoenmakers Berry, Veeningen Meilof 2025-02-20T09:38:17Z 置信度 0.70
• A Review on Privacy Preserving Data Mining using Secure Multiparty Computation
  crossref U. Kumaran, Neelu Khare 2017-02-06T08:18:42Z 置信度 0.70
• Secure Multiparty Computation via Oblivious Polynomial Evaluation
  The number of opportunities for cooperative computation has exponentially been increasing with growing interaction via Internet technologies. These computations could occur between almost trusted partners, between partially trusted partners, or even between co…
  crossref Mert Özarar, Attila Özgit 2013-04-23T04:12:06Z 置信度 0.70
• Secure multiparty computation with a dishonest majority via quantum means
  crossref Klearchos Loukopoulos, Daniel E. Browne 2010-06-28T13:33:14Z 置信度 0.70
• On the Necessity of Rewinding in Secure Multiparty Computation
  crossref Michael Backes, Jörn Müller-Quade, Dominique Unruh 2007-05-16T07:43:44Z 置信度 0.70
• Scalable Secure Multiparty Computation
  crossref Ivan Damgård, Yuval Ishai 2006-09-23T02:21:52Z 置信度 0.70