Privacy aware smart city: A case study in collaborative filtering recommender systems
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.
J. Parallel Distributed Comput.