DPSR: A Differentially Private Social Recommender System for Mobile Users

摘要

Recommender systems, which provide users with suggestions for selecting items that is of potential interest to them, are widely used to assist mobile users in reducing information overload and making better choice quickly in their daily life. Social recommender systems, which have the potential to mitigate the new user cold-start problem, utilize social relationships as an extra source of information. As recommendation results depend on users' individual data, privacy breaches may occur. Although several differentially private social recommender systems have been proposed, their application scopes or protection strengths are limited. In this paper, we propose a differentially private social recommender system for mobile users named DPSR to block curious users from inferring the existence of someone else's numeric rating or social relationship. Empirical evaluations on two real-world datasets are conducted, and the results show that DPSR can balance the utility of recommendations with the privacy of users' data in both normal and cold-start test view.