Towards a More Reliable Privacy-preserving Recommender System
This work addresses privacy concerns for users in distributed recommendation systems, though it is incremental as it builds on existing methods like matrix factorization and differential privacy.
The paper tackles the problem of preserving user privacy in recommender systems by proposing a framework that protects ratings, their existence, and the learned model, achieving differential privacy with minimal accuracy loss.
This paper proposes a privacy-preserving distributed recommendation framework, Secure Distributed Collaborative Filtering (SDCF), to preserve the privacy of value, model and existence altogether. That says, not only the ratings from the users to the items, but also the existence of the ratings as well as the learned recommendation model are kept private in our framework. Our solution relies on a distributed client-server architecture and a two-stage Randomized Response algorithm, along with an implementation on the popular recommendation model, Matrix Factorization (MF). We further prove SDCF to meet the guarantee of Differential Privacy so that clients are allowed to specify arbitrary privacy levels. Experiments conducted on numerical rating prediction and one-class rating action prediction exhibit that SDCF does not sacrifice too much accuracy for privacy.