Achieving Effective $k$ -Anonymity for Query Privacy in Location-Based Services

Abstract

Location-based services (LBS) leveraged by ubiquitous mobile devices have brought great convenience to mobile users in various aspects, including communication, information exchange, social activities, and so on. However, privacy concerns arise at the same time, since the users need to submit their locations and query contents to the LBS servers. To this end, location privacy and query privacy have been recognized. In particular, this paper focuses on query privacy for preventing the leakage of users’ query contents. Cloaking region-based techniques using untrusted servers and client-based $k$ -anonymity approaches have been devised to preserve query privacy in LBS. However, these works suffer from single point of failure or insufficiency of query privacy. To address this issue, we investigate effective $k$ -anonymity-based solutions for query privacy in LBS. We formulate a probabilistic framework $\textit{P}k\textit{A}$ , under which $k$ -anonymity-based mechanisms can be initiated, and analyze a recent proposed algorithm $DLS$ as an instance of $\textit{P}k\textit{A}$ . An algorithm circle segment is presented to provide effective query privacy when query interests have similar prior probability. To obtain more effective query privacy in general cases, we propose two algorithms MEE and MER , which optimize two individual privacy metrics, denoted expected entropy and expected max-min ratio, adopted in this paper. We recognize two practical properties— $No~More~Leakage$ and $k$ - $Effectiveness$ for effective query privacy, and our proposed algorithms satisfy both of $No~More~Leakage$ and $k$ - $Effectiveness$ . We conduct evaluation based on real-life data sets and synthetic distributions of query interests, and the evaluation results demonstrate that our proposed algorithms produce significantly improved query privacy.