| Title |
Privacy-Aware Smart City: A Case Study In Collaborative Filtering Recommender Systems |
| ID_Doc |
43147 |
| Authors |
Zhang, F; Lee, VE; Jin, RM; Garg, S; Choo, KKR; Maasberg, M; Dong, LJ; Cheng, C |
| Year |
2019 |
| Published |
JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING, 127 |
| DOI |
http://dx.doi.org/10.1016/j.jpdc.2017.12.015 |
| 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. (C) 2018 Elsevier Inc. All rights reserved. |
| Author Keywords |
Smart cities; Privacy-preserving collaborative filtering; Recommendation systems; Data privacy; Parallel computing |
