Building a privacy-preserving semantic overlay for Peer-to-Peer networks

Abstract

Searching a Peer-to-Peer (P2P) network without using a central index has been widely investigated but proved to be very difficult. Various strategies have been proposed, however no practical solution to date also addresses privacy concerns. By clustering peers which have similar interests, a semantic overlay provides a method for achieving scalable search. Traditionally, in order to find similar peers, a peer is required to fully expose its preferences for items or content, therefore disclosing this private information. However, in a hostile environment, such as a P2P system, a peer cannot know the true identity or intentions of fellow peers. In this paper, we propose two protocols for building a semantic overlay in a privacy-preserving manner by modifying existing solutions to the Private Set Intersection (PSI) problem. Peers in our overlay compute their similarity to other peers in the encrypted domain, allowing them to find similar peers. Using homomorphic encryption, peers can carrying out computations on encrypted values, without needing to decrypt them first. We propose two protocols, one based on the inner product of vectors, the other on multivariate polynomial evaluation, which are able to compute a similarity value between two peers. Both protocols are implemented on top of an existing P2P platform and are designed for actual deployment. Using a supercomputer and a dataset extracted from a real world instance of a semantic overlay, we emulate our protocols in a network consisting of a thousand peers. Finally, we show the actual computational and bandwidth usage of the protocols as recorded during those experiments.