Title: Towards a More Reliable Privacy-preserving Recommender System,
Abstract: 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.