An efficient Privacy-Preserving Recommender System
Thi Van Anh Vu, Dung Luong-The, Quan Hoang-Van · 2022
The popularity of online recommender systems has soared. They are deployed on numerous websites and gather tremendous amounts of user data that are necessary for recommendation purposes. However, this data may pose a severe threat to user privacy, if accessed by untrusted parties or used inappropriately. The goal of a privacy-preserving recommender system is to hide user ratings from the system and yet allow them to make recommendations. A recent example is the privacy-preserving recommender scheme proposed by Pranav Verma et al. Their scheme can ensure the privacy of user ratings against a malicious server as shown by Mu, Shao, and Miglani. However, this scheme still requires quite high communication and computation costs. This paper proposes an improved protocol using Secure multi-party computation. The theoretical and experimental analysis shows that the proposed method is effective in both computing and communication compared to other method. Moreover, the new protocol preserves the privacy of the honest users against the miner and up to n-2 corrupted users.