A Game Theoretic Approach toward Multi-Party Privacy-Preserving Distributed Data Mining

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. It also presents equilibrium-analysis of such PPDM-games and outlines a game-theoretic solution based on the concept of “cheap-talk” borrowed from the economics and the game theory literature.
Date: September 18, 2007
Book Title: Proceedings of the 11th European Conference on Principles and Practice of Knowledge Discovery in Databases, Warsaw, Poland, September 2007, Pages 523-531, Springer Berlin / Heidelberg
Type: Proceedings
Address: Warsaw, Poland

Bibtex


@Proceedings{A_Game_Theoretic_Approach_toward_Multi_P,
  author = "Hillol Kargupta and Kamalika Das and Kun Liu",
  title = "{A Game Theoretic Approach toward Multi-Party Privacy-Preserving Distributed Data Mining}",
  month = "September",
  year = "2007",
  address = ", Warsaw, Poland",
  booktitle = "Proceedings of the 11th European Conference on Principles and Practice of Knowledge Discovery in Databases, Warsaw, Poland, September 2007, Pages 523-531, Springer Berlin / Heidelberg",
}