TR-CS _ 01 _ 07 A Game Theoretic Approach toward Multi-Party Privacy-Preserving Distributed Data Mining

Analysis of privacy-sensitive data in a multi-party enviro nment 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 thi rd party sensitive data. This paper argues that most of these assumptions fall a part in real-life applications of privacy-preserving distributed data mini ng (PPDM). The paper offers a more realistic formulation of the PPDM problem a s a multi-party game where each party tries to maximize its own objectives. I t develops a game-theoretic framework for developing and analyzing PPD M algorithms. It also presents equilibrium-analysis of such PPDM-games a nd outlines a game-theoretic solution based on the concept of “cheap-tal k” borrowed from the economics and the game theory literature.