P2P collaborative filtering with privacy

With the evolution of the Internet and e-commerce, collaborative filtering (CF) and privacy-preserving collaborative filtering (PPCF) have become popular. The goal in CF is to generate predictions with decent accuracy, efficiently. The main issue in PPCF, however, is achieving such a goal while preserving users privacy. Many implementations of CF and PPCF techniques proposed so far are centralized. In centralized systems, data is collected and stored by a central server for CF purposes. Centralized storage poses several hazards to users because the central server controls users data. In this work, we investigate how to produce naïve Bayesian classifier (NBC)-based recommendations while preserving users privacy without using a central server. In a community of people, users might create a peer-to-peer (P2P) network. Through P2P network, users can communicate with each other and exchange data to produce predictions. We share the workload of prediction process and offer referrals efficiently using P2P network. We propose privacy-preserving schemes and analyze them in terms of accuracy, privacy, and efficiency. Our real data-based results show that our schemes offer accurate NBC-based predictions with privacy eliminating central server