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このアイテムの引用には次の識別子を使用してください: http://hdl.handle.net/10119/9848

タイトル: Privacy-Preserving Data Mining: A Game-theoretic Approach
著者: Miyaji, Atsuko
Rahman, Mohammad Shahriar
キーワード: Privacy-preserving data mining
Set-intersection
Game theory
Computational strict Nash equilibrium
Stability with respect to trembles
発行日: 2011-06-29
出版者: Springer
誌名: Lecture Notes in Computer Science
巻: 6818/2011
開始ページ: 186
終了ページ: 200
DOI: 10.1007/978-3-642-22348-8_15
抄録: Privacy-preserving data mining has been an active research area in recent years due to privacy concerns in many distributed data mining settings. Protocols for privacy-preserving data mining have considered semi-honest, malicious, and covert adversarial models in cryptographic settings, whereby an adversary is assumed to follow, arbitrarily deviate from the protocol, or behaving somewhere in between these two, respectively. Semi-honest model provides weak security requiring small amount of computation, on the other hand, malicious and covert models provide strong security requiring expensive computations like homomorphic encryptions. However, game theory allows us to design protocols where parties are neither honest nor malicious but are instead viewed as rational and are assumed (only) to act in their own self-interest. In this paper, we build efficient and secure set-intersection protocol in game-theoretic setting using cryptographic primitives. Our construction avoids the use of expensive tools like homomorphic encryption and oblivious transfer. We also show that our protocol satisfies computational versions of strict Nash equilibrium and stability with respect to trembles.
Rights: This is the author-created version of Springer, Atsuko Miyaji and Mohammad Shahriar Rahman, Lecture Notes in Computer Science, 6818/2011, 2011, 186-200. The original publication is available at www.springerlink.com, http://dx.doi.org/10.1007/978-3-642-22348-8_15
URI: http://hdl.handle.net/10119/9848
資料タイプ: author
出現コレクション:b10-1. 雑誌掲載論文 (Journal Articles)

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