A Matrix Factorization Recommendation System-Based Local Differential Privacy for Protecting Users’ Sensitive Data
Xiaoyao Zheng, Manping Guan, Xianmin Jia, Liangmin Guo, Yonglong Luo
Abstract
The recommendation system (RS) predicts user ratings by collecting user information, but the users’ private information may be exposed in this process. Thus, it is crucial to achieving a balance between recommendation performance and privacy-preserving of RSs. Aiming to solve the above problem, this article proposes a novel matrix factorization (MF) algorithm. The algorithm predicts the user rating through a linear weighting of global average rating, item average rating, user average rating, and MF, which improves the prediction accuracy. Then, based on the above algorithm, this article proposes a MF RS for preserving user privacy by using local differential privacy technology. In this algorithm, the rating data are normalized on the user side to reduce global sensitivity. Then, Laplace noise is added to sensitive data before it is sent to the aggregator. Finally, based on the disturbed data, rating prediction is realized by using the MF algorithm. The proposed method is compared with five well-known recommendation methods on four public datasets. The experimental results show that the proposed algorithm achieves better recommendation performance at the same privacy-preserving level.