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PKDD
2007
Springer

Multi-party, Privacy-Preserving Distributed Data Mining Using a Game Theoretic Framework

4 years 6 months ago
Multi-party, Privacy-Preserving Distributed Data Mining Using a Game Theoretic Framework
Abstract. Analysis of privacy-sensitive data in a multi-party environment often assumes that the parties are well-behaved and they abide by the protocols. Parties compute whatever is needed, communicate correctly following the rules, and do not collude with other parties for exposing third party’s sensitive data. This paper argues that most of these assumptions fall apart in real-life applications of privacy-preserving distributed data mining (PPDM). This paper offers a more realistic formulation of the PPDM problem as a multi-party game where each party tries to maximize its own objectives. It develops a game-theoretic framework to analyze the behavior of each party in such games and presents detailed analysis of the well known secure sum computation as an example.
Hillol Kargupta, Kamalika Das, Kun Liu
Added 09 Jun 2010
Updated 09 Jun 2010
Type Conference
Year 2007
Where PKDD
Authors Hillol Kargupta, Kamalika Das, Kun Liu
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