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Abstract: We propose an efficient framework for enabling secure multi-party numericalcomputations in a Peer-to-Peer network. This problem arises in a range ofapplications such as collaborative filtering, distributed computation of trustand reputation, monitoring and numerous other tasks, where the computing nodeswould like to preserve the privacy of their inputs while performing a jointcomputation of a certain function.Although there is a rich literature in the field of distributed systemssecurity concerning secure multi-party computation, in practice it is hard todeploy those methods in very large scale Peer-to-Peer networks. In this work,we examine several possible approaches and discuss their feasibility. Among thepossible approaches, we identify a single approach which is both scalable andtheoretically secure.An additional novel contribution is that we show how to compute theneighborhood based collaborative filtering, a state-of-the-art collaborativefiltering algorithm, winner of the Netflix progress prize of the year 2007. Oursolution computes this algorithm in a Peer-to-Peer network, using a privacypreserving computation, without loss of accuracy.Using extensive large scale simulations on top of real Internet topologies,we demonstrate the applicability of our approach. As far as we know, we are thefirst to implement such a large scale secure multi-party simulation of networksof millions of nodes and hundreds of millions of edges.



Author: Danny Bickson, Genia Bezman, Danny Dolev, Benny Pinkas

Source: https://arxiv.org/



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