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Privacy-aware smart city: a case study in collaborative filtering recommender systems
journal contributionposted on 2023-05-19, 17:03 authored by Zhang, F, Lee, VE, Jin, R, Saurabh GargSaurabh Garg, Choo, K-KR, Maasberg, M, Dong, L, Cheng, C
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 κ-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 κ-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 κ-anonymity-like data privacy, and enhance data utility (quantified using prediction accuracy in this paper). We show that the optimal κ-coRated mapping is an NP-hard problem and design an efficient greedy algorithm to achieve κ-coRating. We then demonstrate the utility of our approach empirically.
Publication titleJournal of Parallel and Distributed Computing
Department/SchoolSchool of Information and Communication Technology
PublisherAcademic Press Inc Elsevier Science
Place of publication525 B St, Ste 1900, San Diego, USA, Ca, 92101-4495
Rights statementCopyright 2018 Elsevier Inc.