A local distributed peer-to-peer algorithm using multi-party optimization based privacy preservation for data mining primitive computation

Kamalika Das, Hillol Kargupta, Kanishka Bhaduri
2009 2009 IEEE Ninth International Conference on Peer-to-Peer Computing  
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 therefore, is highly scalable. It particularly deals with the distributed computation of the sum
more » ... f 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 privacypreserving clustering, frequent itemset mining, and statistical aggregate computation. * Also affiliated to AGNIK LLC, MD, USA 1
doi:10.1109/p2p.2009.5284514 dblp:conf/p2p/DasBK09 fatcat:jwbgdjujvbbelmblpujikmrssy