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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 therefore, is highly scalable. It particularly deals with the distributed computation of the sumdoi:10.1109/p2p.2009.5284514 dblp:conf/p2p/DasBK09 fatcat:jwbgdjujvbbelmblpujikmrssy