A Method of Personalized Recommendation Based on Differential Privacy

Abstract

Research shows that an attacker can predict the user's rating record by observing the recommended results of the recommendation system, which will pose a great threat to the privacy of the user. Traditional recommendation system built on a collaborative filtering approach; it is difficult to provide a strict privacy protection. In this paper, a personalized recommendation method based on differential privacy preserving (DPP) is proposed. Bayesian network is used to optimize the addition of differential privacy noise, which can balance the data availability and privacy. Meanwhile, add the external attributes to construct the Bayesian network to semantically verify the recommended results, improve the recommended accuracy rate. The experimental results verified the feasibility and effectiveness of the method.