Privacy-aware smart city: A case study in collaborative filtering recommender systems

Abstract

Ensuring privacy in recommender systems for smart cities remains a research challenge, and in this paper we study collaborative filtering recommender systems for privacy-aware smart cities. Specifically, we use the rating matrix to establish connections between a privacy-aware smart city and k-coRating, a novel privacy-preserving rating data publishing model. First, we model privacy concerns in a smart city as the problem of privacy-preserving collaborative filtering recommendation. Then, we introduce k-coRating to address privacy concerns in published rating matrices, by filling the null ratings with predicted scores. This allows us to mask the original ratings to preserve k-anonymity-like data privacy, and enhance data utility (quantified using prediction accuracy in this paper). We show that the optimal k-coRated mapping is an NP-hard problem and design an efficient greedy algorithm to achieve k-coRating. We then demonstrate the utility of our approach empirically.