Privacy-Preserving and yet Robust Collaborative Filtering Recommender as a Service

Abstract

In this paper, we provide a general system structure for latent factor based collaborative filtering recommenders by formulating them into model training and prediction computing stages. Aiming at pragmatic solutions, we first show how to construct privacy-preserving and yet robust model training stage based on existing solutions. Then, we propose two cryptographic protocols to realize a privacy-preserving prediction computing stage, depending on whether or not an extra proxy is involved.