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

Authors:
Hillol Kargupta;Kamalika Das;Kun Liu
Affiliations:
University of Maryland, Baltimore County, Baltimore MD 21250, USA and Agnik, LLC, USA;University of Maryland, Baltimore County, Baltimore MD 21250, USA;IBM Almaden Research Center, San Jose CA 95120, USA
Venue:
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Year:
2007

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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.