Item-Based Privacy-Preserving Recommender System with Offline Users and Reduced Trust Requirements

Safeguarding privacy of ratings assigned by users is an important issue for recommender systems. There are several existing protocols that allow a server to generate recommendations from homomorphically encrypted ratings, thereby ensuring privacy of rating data. After collecting the encrypted ratings, the server may require further interaction with each user, which is problematic in case some users were to go offline. To solve the offline user problem previous solutions use additional semi-honest third parties. In this paper, we propose a privacy-preserving recommender system that does not suffer from the offline user problem. Unlike previous works, our proposal does not require any additional third party. We demonstrate with the help of experiments that the time required to generate recommendations is efficient for practical applications.

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