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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 sensitive data. This paper argues that most of these assumptions fall apart in real-life applications of Privacy-Preserving Distributed Data mining (PPDM). The 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 offers a game-theoretic framework for developing and analyzing new robust PPDM algorithms.
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