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

journal contribution
posted 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 title

Journal of Parallel and Distributed Computing








School of Information and Communication Technology


Academic Press Inc Elsevier Science

Place of publication

525 B St, Ste 1900, San Diego, USA, Ca, 92101-4495

Rights statement

Copyright 2018 Elsevier Inc.

Repository Status

  • Restricted

Socio-economic Objectives

Information systems, technologies and services not elsewhere classified

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