A Matrix Factorization Recommendation System-Based Local Differential Privacy for Protecting Users’ Sensitive Data

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

Computer security · Data mining · Differential privacy · Information privacy · Information retrieval · Internet privacy · Matrix decomposition · Privacy Protection · Recommender system · Computer Science · Privacy-Preserving Technologies in Data · Privacy, Security, and Data Protection · Recommender Systems and Techniques

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