Peer-to-Peer Networking and Applications

, Volume 4, Issue 2, pp 192–209

Multi-objective optimization based privacy preserving distributed data mining in Peer-to-Peer networks

Authors

    • Stinger Ghaffarian Technologies Inc.NASA Ames Research Center
  • Kanishka Bhaduri
    • Mission Critical Technologies Inc.NASA Ames Research Center
  • Hillol Kargupta
    • CSEE Dept.University of Maryland
    • AGNIK LLC
Article

DOI: 10.1007/s12083-010-0075-1

Cite this article as:
Das, K., Bhaduri, K. & Kargupta, H. Peer-to-Peer Netw. Appl. (2011) 4: 192. doi:10.1007/s12083-010-0075-1

Abstract

This paper proposes a scalable, local privacy-preserving algorithm for distributed Peer-to-Peer (P2P) data aggregation useful for many advanced data mining/analysis tasks such as average/sum computation, decision tree induction, feature selection, and more. Unlike most multi-party privacy-preserving data mining algorithms, this approach works in an asynchronous manner through local interactions and it is highly scalable. It particularly deals with the distributed computation of the sum of a set of numbers stored at different peers in a P2P network in the context of a P2P web mining application. The proposed optimization-based privacy-preserving technique for computing the sum allows different peers to specify different privacy requirements without having to adhere to a global set of parameters for the chosen privacy model. Since distributed sum computation is a frequently used primitive, the proposed approach is likely to have significant impact on many data mining tasks such as multi-party privacy-preserving clustering, frequent itemset mining, and statistical aggregate computation.

Keywords

Privacy preservingData miningPeer-to-Peer

Copyright information

© Springer Science + Business Media, LLC 2010