Providing private recommendations using naive Bayesian classifier

Abstract

Today's CF systems fail to protect users' privacy. Without privacy protection, it becomes a challenge to collect sufficient and high quality data for CF. With privacy protection, users feel comfortable to provide more truthful and dependable data. In this paper, we propose to employ randomized response techniques (RRT) to protect users privacy while producing accurate referrals using naive Bayesian classifier (NBC), which is one of the most successful learning algorithms. We perform various experiments using real data sets to evaluate our privacy-preserving schemes.