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:	Refereed Article
Keywords:	privacy, Security, IoT
Research Division:	Information and Computing Sciences
Research Group:	Distributed Computing
Research Field:	Distributed and Grid Systems
Objective Division:	Information and Communication Services
Objective Group:	Computer Software and Services
Objective Field:	Application Tools and System Utilities
UTAS Author:	Garg, S (Dr Saurabh Garg)
ID Code:	125154
Year Published:	2019
Web of Science® Times Cited:	6
Deposited By:	Information and Communication Technology
Deposited On:	2018-04-03
Last Modified:	2020-05-18
Downloads:	0