An Enhanced Privacy-Preserving Recommender System
A recommender system stores historical data collected over a long period from various users, these are used to predict how new and existing users would rate an item. As user data is stored by the system, this poses threat to user’s privacy. The goal of a privacy preserving recommender system is to hide user ratings from system and yet allow to make recommendations.
A recent example is the privacy-preserving recommender scheme proposed by Badsha, Yi and Khalil. Their scheme assumes that the server is semi-honest. However, when the server is malicious an attack is possible, as shown by Mu, Shao and Miglani. In this paper, we propose a simple modification to their scheme, which preserves the privacy of ratings against a malicious server. We demonstrate that the computation and communication costs of modified protocol are reasonable in comparison to original protocol.
KeywordsRecommender system Privacy Collaborative Filtering Content Based Filtering Homomorphic encryption
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