Abstract
In mobile Internet, popular Location-Based Services (LBSs) recommend Point-of-Interest (POI) data according to physical positions of smartphone users. However, untrusted LBS providers can violate location privacy by analyzing user requests semantically. Therefore, this paper aims at protecting user privacy in location-based applications by evaluating disclosure risks on sensitive location semantics. First, we introduce a novel method to model location semantics for user privacy using Bayesian inference and demonstrate details of computing the semantic privacy metric. Next, we design a cloaking region construction algorithm against the leakage of sensitive location semantics. Finally, a series of experiments evaluate this solution’s performance to show its availability.
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CITATION STYLE
Wu, Z., Chen, Z., Zhu, J., Sun, H., & Guan, Z. (2015). Location semantics protection based on bayesian inference. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9098, pp. 297–308). Springer Verlag. https://doi.org/10.1007/978-3-319-21042-1_24
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