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

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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.

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Correspondence to Kamalika Das.

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A shorter version of this paper was published in IEEE P2P’09 conference. This work was supported by AFOSR MURI grant 2009-11.

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Das, K., Bhaduri, K. & Kargupta, H. Multi-objective optimization based privacy preserving distributed data mining in Peer-to-Peer networks. Peer-to-Peer Netw. Appl. 4, 192–209 (2011).

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