How to deal with malicious users in privacy‐preserving distributed data mining

DOI10.1002/sam.10029zbMath1178.68224OpenAlexW4243616933MaRDI QIDQ3497744

Yitao Duan, John F. Canny

Publication date: 27 July 2009

Published in: Statistical Analysis and Data Mining: The ASA Data Science Journal (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1002/sam.10029

zbMATH Keywords

L2-normprivacyvector additionzero-knowledge protocolmalicious users

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Data encryption (aspects in computer science) (68P25)

Related Items (1)

Pufferfish

Cites Work

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• Definitions and properties of zero-knowledge proof systems
• Player simulation and general adversary structures in perfect multiparty computation
• Simplified VSS and fast-track multiparty computations with applications to threshold cryptography
• How To Prove Yourself: Practical Solutions to Identification and Signature Problems
• The Knowledge Complexity of Interactive Proof Systems
• Foundations of Cryptography
• Efficient noise-tolerant learning from statistical queries
• Complete characterization of adversaries tolerable in secure multi-party computation (extended abstract)
• Theory of Cryptography
• Privacy preserving data mining

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