PrivRec: User-Centric Differentially Private Collaborative Filtering Using LSH and KD

The collaborative filtering (CF)-based recommender systems provide recommendations by collecting users’ historical ratings and predicting their preferences on new items. However, this inevitably brings privacy concerns since the collected data might reveal sensitive information of users, when training a recommendation model and applying the trained model (i.e., testing the model). Existing differential privacy (DP)-based approaches generally have non-negligible trade-offs in recommendation utility, and often serve as centralized server-side approaches that overlook the privacy during testing when applying the trained models in practice. In this paper, we propose PrivRec, a user-centric differential private collaborative filtering approach, that provides privacy guarantees both intuitively and theoretically while preserving recommendation utility. PrivRec is based on the locality sensitive hashing (LSH) and the teacher-student knowledge distillation (KD) techniques. A teacher model is trained on the original user data without privacy constraints, and a student model learns from the hidden layers of the teacher model. The published student model is trained without access to the original user data and takes the locally processed data as input for privacy. The experimental results on real-world datasets show that our approach provides promising utility with privacy guarantees compared to the commonly used approaches.