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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 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.

Item Type:	Article
Authors/Creators:	Zhang, F and Lee, VE and Jin, R and Garg, S and Choo, K-KR and Maasberg, M and Dong, L and Cheng, C
Keywords:	privacy, Security, IoT
Journal or Publication Title:	Journal of Parallel and Distributed Computing
Publisher:	Academic Press Inc Elsevier Science
ISSN:	0743-7315
DOI / ID Number:	https://doi.org/10.1016/j.jpdc.2017.12.015
Copyright Information:	Copyright 2018 Elsevier Inc.
Related URLs:	Google Scholar: Garg, S UTAS Profile: Garg, S
Item Statistics:	View statistics for this item

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