Software Engineering White Papers
Multi-Party, Privacy-Preserving Distributed Data Mining Using a Game Theoretic Framework
Overview 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 ex-posing 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.
| Publisher | IBM | File Format | |
|---|---|---|---|
| Date Published | August 2007 | ||
| Format | White Papers | ||
| Topics | |||
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