P2P collaborative filtering with privacy

Authors : Cihan KALELİ, Hüseyin POLAT

Pages : 101-116

View : 9 | Download : 6

Publication Date : 0000-00-00

Article Type : Research Paper

Abstract :With the evolution of the Internet and e-commerce, collaborative filtering insert ignore into journalissuearticles values(CF); and privacy-preserving collaborative filtering insert ignore into journalissuearticles values(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 insert ignore into journalissuearticles values(NBC);-based recommendations while preserving users` privacy without using a central server. In a community of people, users might create a peer-to-peer insert ignore into journalissuearticles values(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.
Keywords : Privacy, P2P, collaborative filtering, naïve bayesian classifier, accuracy