Peer-to-peer data mining, privacy issues, and games

Authors:
Kanishka Bhaduri;Kamalika Das;Hillol Kargupta
Affiliations:
Computer Science and Electrical Engineering Department, University of Maryland, Baltimore County, Baltimore, MD;Computer Science and Electrical Engineering Department, University of Maryland, Baltimore County, Baltimore, MD;Computer Science and Electrical Engineering Department, University of Maryland, Baltimore County, Baltimore, MD
Venue:
AIS-ADM'07 Proceedings of the 2nd international conference on Autonomous intelligent systems: agents and data mining
Year:
2007

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Abstract

Peer-to-Peer (P2P) networks are gaining increasing popularity in many distributed applications such as file-sharing, network storage, web caching, searching and indexing of relevant documents and P2P network-threat analysis. Many of these applications require scalable analysis of data over a P2P network. This paper starts by offering a brief overview of distributed data mining applications and algorithms for P2P environments. Next it discusses some of the privacy concerns with P2P data mining and points out the problems of existing privacy-preserving multi-party data mining techniques. It further points out that most of the nice assumptions of these existing privacy preserving techniques 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 and points out some recent results.