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• Secure Multiparty Computation
  MPC has moved from theoretical study to real-world usage. How is it doing?
  crossref Yehuda LINDELL 2022-11-30T12:07:35Z 置信度 0.70
• Mechanism Design and Strong Truthfulness
  In this chapter we give a very brief overview of some fundamentals from mechanism design, the branch of game theory dealing with designing protocols to cope with agents' private incentives and selfish behavior. We also present recent results involving a new, e…
  crossref Giannakopoulos Yiannis 2025-02-20T09:38:17Z 置信度 0.70
• Secure multiparty quantum aggregating protocol
  Secure multiparty quantum computation is an important and essential paradigm of quantum computing. All the existing aggregating protocols are $(n, n)$ threshold approaches, where $n$ represents the total number of players. If one player is dishonest, the aggre…
  crossref Kartick Sutradhar 2023-01-29T20:26:03Z 置信度 0.70
• Secure Multiparty Computation for Privacy Preserving Data Mining
  The increasing use of data-mining tools in both the public and private sectors raises concerns regarding the potentially sensitive nature of much of the data being mined. The utility to be gained from widespread data mining seems to come into direct conflict w…
  crossref Yehida Lindell 2011-05-24T12:13:07Z 置信度 0.70
• Structural Framing of Protocol for Secure Multiparty Cloud Computation
  crossref Nikhar Maheshwari, Krati Kiyawat 2011-08-03T21:31:16Z 置信度 0.70
• REALIZING SECURE MULTIPARTY COMPUTATION ON INCOMPLETE NETWORKS
  crossref 2011-02-22T02:48:24Z 置信度 0.70
• Unconditionally Secure Multiparty Computation from Noisy Resources
  crossref Stefan Wolf, Jürg Wullschleger 2007-10-24T01:06:58Z 置信度 0.70
• Synthetic Data1
  crossref Trivellore Raghunathan 2024-08-23T14:36:41Z 置信度 0.70
• An efficient simulation for quantum secure multiparty computation.
  Abstract The quantum secure multiparty computation is one of the important properties of secure quantum communication. In this paper, we propose a quantum secure multiparty summation (QSMS) protocol based on ( t , n ) threshold approach, which can be used in m…
  europepmc Kartick Sutradhar, Hari Om 2021 置信度 0.80
• Secure Multiparty Computation for Privacy-Preserving Data Mining
  In this paper, we survey the basic paradigms and notions of secure multiparty computation and discuss their relevance to the field of privacy-preserving data mining. In addition to reviewing definitions and constructions for secure multiparty computation, we d…
  crossref Yehuda Lindell, Benny Pinkas 2018-02-27T14:41:23Z 置信度 0.70
• Secure multiparty computation using secret sharing
  crossref Kinjal Patel 2017-07-10T21:24:34Z 置信度 0.70
• Differential Privacy and Cryptography
  crossref Xi He 2024-08-23T14:36:41Z 置信度 0.70
• Towards Optimal Non-interactive Secure Multiparty Computation for Abelian Programs
  crossref Maki Yoshida 2024-08-19T13:25:01Z 置信度 0.70
• Public Watermark Detection Using Secure Multiparty Computation
  crossref Hong Wang, Shimin Wei 2011-07-13T15:57:57Z 置信度 0.70
• Secure Multiparty Computation Using Secure Virtual Machines
  The development of new processor capabilities which enable hardware-based memory encryption, capable of isolating and encrypting application code and data in memory, have led to the rise of confidential computing techniques that protect data when processed on …
  crossref Danko Miladinović, Adrian Milaković, Maja Vukasović, Žarko Stanisavljević 等 2024-03-05T08:35:54Z 置信度 0.70
• Handbook of Sharing Confidential Data
  crossref Jörg Drechsler, Daniel Kifer, Jerome Reiter, Aleksandra Slavković 2024-08-23T14:36:41Z 置信度 0.70
• Secure Multiparty Computation and the GDPR: Processing of Personal Data and Joint Controllership
  crossref Piotr Rataj, Nils Wiedemann 2025-10-06T19:16:05Z 置信度 0.70
• Secure multiparty computation based privacy preserving smart metering system
  crossref Cory Thoma, Tao Cui, Franz Franchetti 2012-10-24T16:33:19Z 置信度 0.70
• Scalable and Unconditionally Secure Multiparty Computation
  crossref Ivan Damgård, Jesper Buus Nielsen 2007-08-09T09:51:33Z 置信度 0.70
• Secure Multiparty Computation in arbitrary rings
  crossref Mihai Prunescu 2022-02-10T20:28:53Z 置信度 0.70
• Query Answering for Tabular Data
  crossref Ryan McKenna 2024-08-23T14:36:41Z 置信度 0.70
• Privacy Preserving Deep Learning using Secure Multiparty Computation
  crossref Suhel Sayyad 2020-09-01T17:03:37Z 置信度 0.70
• Analysis of Data in Secure Multiparty Computation
  crossref Zulfa Shaikh, D.M. Puntambekar, Pushpa Pathak, Dinesh Bhati 2009-03-31T14:37:00Z 置信度 0.70
• Secure Multiparty Computation with Identifiable Abort and Fairness
  crossref Long Nie, ShaoWen Yao, Jing Liu 2023-09-05T17:38:18Z 置信度 0.70
• PMDP: A Secure Multiparty Computation Framework for Maintaining Multiparty Data Privacy in Cloud Computing
  crossref Venkata Surya Bhavana Harish Gollavilli Venkata Surya Bhavana Harish Gollavilli 2024-07-03T06:55:41Z 置信度 0.70
• Secure multiparty quantum computation based on Lagrange unitary operator
  Abstract As an important subtopic of classical cryptography, secure multiparty quantum computation allows multiple parties to jointly compute their private inputs without revealing them. Most existing secure multiparty computation protocols have the shortcomin…
  crossref Xiuli Song, Rui Gou, Aijun Wen 2020-05-13T10:02:50Z 置信度 0.70
• Lower Bounds on the Bottleneck Complexity of Secure Multiparty Computation
  Secure multiparty computation (MPC) is a cryptographic primitive which enables multiple parties to jointly compute a function without revealing any extra information on their private inputs. Bottleneck complexity is an efficiency measure that captures the load…
  crossref Reo Eriguchi, Keitaro Hiwatashi 2026-01-08T23:39:47Z 置信度 0.70
• Secure error correction using multiparty computation
  crossref Mohammad G. Raeini, Mehrdad Nojoumian 2018-03-05T22:14:26Z 置信度 0.70
• Secure multiparty graph computation
  crossref Varsha Bhat Kukkala, S.R.S Iyengar, Jaspal Singh Saini 2016-03-30T00:54:20Z 置信度 0.70
• Privacy Centric Engagement Models Harnessing Federated Learning and Secure Multiparty Computation for Safe Inclusive Digital Ecosystem Interactions
  crossref Chaitran Chakilam, Narayanasamy S, M. Jamuna Rani, Satvik Vats 等 2026-03-05T20:02:37Z 置信度 0.70
• Machine Learning with Differential Privacy
  crossref Anand D. Sarwate 2024-08-23T14:36:41Z 置信度 0.70
• Tutorial: Secure Multiparty Computation for Cloud Computing Paradigm by Durgesh Kumar Mishra
  crossref 2011-01-28T20:12:36Z 置信度 0.70
• Fostering the Uptake of Secure Multiparty Computation in E-Commerce
  crossref Octavian Catrina, Florian Kerschbaum 2008-05-29T17:54:16Z 置信度 0.70
• Introduction to secret sharing based secure multiparty computation in health data.
  Background Joint analyses across multiple health datasets can increase statistical power and improve the generalisability of research findings. However, limitations on data sharing often prevent researchers from fully realising these benefits. Existing approac…
  europepmc Steven Kerr, Daniel Escudero 2026 置信度 0.80
• Secure federated transfer learning with enhanced secure multiparty computation for privacy preserving smart EHR systems.
  europepmc Tae Hoon Kim, C. Rohith Bhat, Temesgen Engida Yimer 2025 置信度 0.80
• Enabling health data analyses across multiple private datasets with no information sharing using secure multiparty computation.
  The UK’s health datasets are among the most comprehensive and inclusive globally, enabling groundbreaking research during the COVID-19 pandemic. However, restrictions on data sharing between secure data environments (SDEs) imposed limitations on the ability to…
  europepmc Steven Kerr, Chris Robertson, Cathie Sudlow, Aziz Sheikh 2025 置信度 0.80
• Privacy-friendly evaluation of patient data with secure multiparty computation in a European pilot study.
  Abstract In multicentric studies, data sharing between institutions might negatively impact patient privacy or data security. An alternative is federated analysis by secure multiparty computation. This pilot study demonstrates an architecture and implementatio…
  europepmc Hendrik Ballhausen, Stefanie Corradini, Claus Belka, Dan Bogdanov 等 2024 置信度 0.80
• Secure Comparisons of Single Nucleotide Polymorphisms Using Secure Multiparty Computation: Method Development.
  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…
  europepmc Andrew Woods, Skyler T Kramer, Dong Xu, Wei Jiang 2023 置信度 0.80
• An Integration Framework of Secure Multiparty Computation and Deep Neural Network for Improving Drug-Drug Interaction Predictions.
  europepmc Liang Pan, Xia Xiao, Shengyun Liu, Shaoliang Peng 2023 置信度 0.80
• Sequre: a high-performance framework for secure multiparty computation enables biomedical data sharing.
  Abstract Secure multiparty computation (MPC) is a cryptographic tool that allows computation on top of sensitive biomedical data without revealing private information to the involved entities. Here, we introduce Sequre, an easy-to-use, high-performance framewo…
  europepmc Haris Smajlović, Ariya Shajii, Bonnie Berger, Hyunghoon Cho 等 2023 置信度 0.80
• EasySMPC: a simple but powerful no-code tool for practical secure multiparty computation.
  Abstract 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 us…
  europepmc Felix Nikolaus Wirth, Tobias Kussel, Armin Müller, Kay Hamacher 等 2022 置信度 0.80
• Randomized Oblivious Transfer for Secure Multiparty Computation in the Quantum Setting.
  Secure computation is a powerful cryptographic tool that encompasses the evaluation of any multivariate function with arbitrary inputs from mutually distrusting parties. The oblivious transfer primitive serves is a basic building block for the general task of …
  europepmc Bruno Costa, Pedro Branco, Manuel Goulão, Mariano Lemus 等 2021 置信度 0.80
• Secure multiparty computation for privacy-preserving drug discovery.
  Abstract Motivation Quantitative structure–activity relationship (QSAR) and drug–target interaction (DTI) prediction are both commonly used in drug discovery. Collaboration among pharmaceutical institutions can lead to better performance in both QSAR and DTI p…
  europepmc Rong Ma, Yi Li, Chenxing Li, Fangping Wan 等 2020 置信度 0.80
• A Privacy-Preserving Log-Rank Test for the Kaplan-Meier Estimator With Secure Multiparty Computation: Algorithm Development and Validation.
  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…
  europepmc Marcel von Maltitz, Hendrik Ballhausen, David Kaul, Daniel F Fleischmann 等 2021 置信度 0.80
• Privacy-preserving record linkage in large databases using secure multiparty computation.
  europepmc Peeter Laud, Alisa Pankova 2018 置信度 0.80
• Secure multiparty computation of a comparison problem.
  europepmc Xin Liu, Shundong Li, Jian Liu, Xiubo Chen 等 2016 置信度 0.80
• Secure multi-party biometric verification using QKD assisted quantum oblivious transfer.
  europepmc 2026 置信度 0.80
• A blockchain-enabled framework for secure and efficient data transmission in underwater sensor networks using advanced cryptographic techniques.
  europepmc 2026 置信度 0.80
• MHBDS: Healthcare blockchain data sharing scheme based on SMPC and TRP-PBFT.
  europepmc 2026 置信度 0.80
• A Blockchain-Based Federated Learning Approach with Secure Third-Party Computation System for Securing Electronic Health Records
  Abstract The secure, efficient, and privacy-preserving management of Electronic Health Records (EHRs) remains a critical challenge as healthcare systems increasingly depend on digital infrastructures. There is a centralized EHR storage model, which is suscepti…
  europepmc Munusamy S, Jothi K R 2026 置信度 0.80
• Evaluating Privacy-Preserving Data Analysis Techniques for Encrypted Health Records
  europepmc 2025 置信度 0.80
• Secure distributed multiple imputation enables missing data inference for private data proprietors.
  europepmc 2026 置信度 0.80
• BioVault: A privacy-first data visitation platform for equitable global collaboration in biomedicine
  europepmc 2026 置信度 0.80
• Assessing the Evolution and Influence of Medical Open Databases on Biomedical Research and Health Care Innovation: A 25-Year Perspective With a Focus on Privacy and Privacy-Enhancing Technologies.
  europepmc 2026 置信度 0.80
• Privacy and Security Throughout the Health Data Life Cycle: From Primary Care to Research Networks.
  pubmed Malin BA, Yan C, Bonomi L 2026 置信度 0.82
• Trust By Design: Enabling Responsible Precision Health Through Blockchain-Powered Digital Twins And Trusted AI.
  europepmc 2025 置信度 0.80
• Fast and Secure Multiparty Querying over Federated Graph Databases.
  Abstract We have developed a framework for efficient privacy preserving multi-party querying (PPMQ) over federated graph databases, leveraging Secure Multi-Party Computation (SMPC) protocols to enhance data security. The system offers two distinct security pro…
  europepmc Nouf Aljuaid, Alexei Lisitsa, Sven Schewe 2025 置信度 0.80
• EUPID-configurable privacy-preserving record linkage in federated health data spaces.
  europepmc 2026 置信度 0.80
• Privacy-Aware Optimization Algorithms for Distributed AI
  europepmc 2025 置信度 0.80
• Efficient multi-party private set union resistant to maximum collusion attacks.
  europepmc 2026 置信度 0.80
• Quantum anonymous selection and its applications.
  europepmc 2025 置信度 0.80
• Efficient data replication in distributed clouds via quantum entanglement algorithms.
  europepmc 2026 置信度 0.80
• Clifti-GPT: Privacy-preserving federated fine-tuning and transferable inference of foundation models on clinical single-cell data
  europepmc 2025 置信度 0.80
• Cryptanalysis of efficient controlled semi-quantum secret sharing protocol with entangled state.
  europepmc 2025 置信度 0.80
• Overcoming collaboration barriers in quantitative trait loci analysis.
  europepmc 2025 置信度 0.80
• A novel quantum private query protocol and its application in private set intersection.
  europepmc 2025 置信度 0.80
• Secure Aggregation Protocols in Federated AI for Anonymized Health Data
  europepmc 2025 置信度 0.80
• Technical and legal aspects of federated learning in bioinformatics: applications, challenges and opportunities.
  pubmed Malpetti D, Scutari M, Gualdi F, van Setten J 等 2025 置信度 0.82
• Towards an improved efficient leakage-resilient enhanced private set union.
  europepmc 2026 置信度 0.80
• Federated Learning for Privacy-Preserving Defense in Power Cyber-Physical Systems: Frameworks, Techniques, and Challenges
  The increasing interconnection and digitalization of modern energy systems have intensified cybersecurity vulnerabilities in Power Cyber-Physical Systems (Power CPS). Traditional centralized defense approaches struggle to balance privacy preservation, scalabil…
  europepmc Xiaokang Wang 2025 置信度 0.80 Cyber-physical systemComputer securityComputer sciencePower (physics)Internet privacy
• Nine quick tips for trustworthy machine learning in the biomedical sciences.
  europepmc 2025 置信度 0.80
• Privacy-by-Design Framework for Large Language Model Chatbots in Urology.
  pubmed Kim EJ, Kim J 2025 置信度 0.82
• DP-BREM: Differentially-Private and Byzantine-Robust Federated Learning with Client Momentum.
  europepmc 2025 置信度 0.80
• Blockchain-enabled federated learning with edge analytics for secure and efficient electronic health records management.
  europepmc 2025 置信度 0.80
• Simplifying Multiparty Computation: A Client-Driven Metaprotocol for Federated Secure Computing.
  Introduction: Secure Multi-Party Computation (SMPC) offers a powerful tool for collaborative healthcare research while preserving patient data privacy. State of the art: However, existing SMPC frameworks often require separate executions for each desired compu…
  europepmc Johanna Schwinn, Hendrik Ballhausen, Seyedmostafa Sheikhalishahi, Matthaeus Morhart 等 2024 置信度 0.80
• Privacy and security enhancement in smart cities using advanced cryptographic techniques.
  europepmc 2026 置信度 0.80
• Post-Quantum Cryptography Resilience in Telehealth Using Quantum Key Distribution.
  europepmc 2025 置信度 0.80
• A Universal Metric of Dataset Similarity for Cross-silo Federated Learning.
  europepmc 2025 置信度 0.80
• Multiparty private summation protocol based on two-state quantum-mechanical system.
  europepmc 2025 置信度 0.80
• Integration of Federated Learning and Blockchain in Health Care: Tutorial on Medical Data, Architectures, Privacy, Security, and Regulatory Compliance.
  The convergence of artificial intelligence (AI), blockchain technology, and health care represents one of the most transformative yet technically challenging frontiers in computational medicine. As health care systems adopt data-driven paradigms for precision …
  pubmed Shahsavari Y, Baseri Y, Hafid A, Dambri OA 等 2026 置信度 0.82
• Leveraging quantum blockchain for secure multiparty space sharing and authentication on specialized metaverse platform.
  europepmc Esmot Ara Tuli, Jae-Min Lee, Dong-Seong Kim 2024 置信度 0.80
• Secure healthcare data management using federated learning, blockchain, and explainable artificial intelligence: a systematic review.
  Due to the rapid digitization of healthcare systems, there has been a huge collection of sensitive personal data of patients. Thus, secure, privacy-preserving, and efficient data management systems are required. Current distributed healthcare systems increasin…
  pubmed Bhardwaj T, Sumangali K 2026 置信度 0.82
• Federated Cross-Incremental Self-Supervised Learning for Medical Image Segmentation.
  europepmc 2025 置信度 0.80
• Instantiating the Hash-then-evaluate paradigm: Strengthening PRFs, PCFs, and OPRFs.
  europepmc 2025 置信度 0.80
• The next frontier in sepsis: connected ICU data for real-world clinical decision making.
  pubmed Carbajo RS, Palma J, Martin-Loeches I 2026 置信度 0.82
• A hybrid federated learning framework with generative AI for privacy-preserving and sustainable security in IOT-enabled smart environments.
  europepmc 2026 置信度 0.80
• Genomic privacy and security in the era of artificial intelligence and quantum computing.
  The rapid advancements in sequencing technologies have greatly increased access to genomic data stored in public databases. This has raised significant privacy and security concerns. This review emphasizes the importance of protecting genomic data by analyzing…
  pubmed Annan R, Noland J, Perkins K, Yuan X 等 2025 置信度 0.82
• Hybrid quantum enhanced federated learning for cyber attack detection.
  europepmc 2024 置信度 0.80
• Data security storage and transmission framework for AI computing power platforms.
  europepmc 2026 置信度 0.80
• MedShieldFL-a privacy-preserving hybrid federated learning framework for intelligent healthcare systems.
  europepmc 2025 置信度 0.80
• A privacy-preserving multi-user retrieval system for multimodal artificial intelligence.
  europepmc 2026 置信度 0.80
• Federated Learning in Healthcare Ethics: A Systematic Review of Privacy-Preserving and Equitable Medical AI.
  pubmed Mir BA, Abbas SR, Lee SW 2026 置信度 0.82
• Both ends of artificial intelligence impacting privacy: a review of violation and protection.
  pubmed Voloch N, Hirschprung RS 2026 置信度 0.82
• Addressing contemporary threats in anonymised healthcare data using privacy engineering.
  europepmc 2025 置信度 0.80
• Toward owner governance in genomic data privacy with Governome.
  europepmc 2025 置信度 0.80
• Integrating advanced neural network architectures with privacy enhanced encryption for secure and intelligent healthcare analytics.
  europepmc 2025 置信度 0.80
• Design of a multi-layered privacy-preserving architecture for secure medical data exchange in cloud environments.
  europepmc 2026 置信度 0.80
• On the Convergence of Internet of Things and Decentralized Finance: Security Challenges and Future Directions.
  europepmc 2026 置信度 0.80
• IPP-DMS: A scalable privacy-preserving data management system for secure and efficient handling of large-scale datasets.
  europepmc 2025 置信度 0.80
• An efficient tripartite remote state preparation scheme with noise analysis.
  europepmc 2026 置信度 0.80