Location Semantics Protection Based on Bayesian Inference

  • Zhengang WuEmail author
  • Zhong Chen
  • Jiawei Zhu
  • Huiping Sun
  • Zhi Guan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9098)


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.


Location privacy protection Location semantics Bayesian inference Spatial cloaking 


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Zhengang Wu
    • 1
    • 2
    Email author
  • Zhong Chen
    • 1
  • Jiawei Zhu
    • 1
  • Huiping Sun
    • 1
  • Zhi Guan
    • 1
  1. 1.Institute of Software, School of EECS, MoE Key Lab of High Confidence Software Technologies (PKU), MoE Key Lab of Network and Software Security Assurance (PKU)Peking University (PKU)BeijingChina
  2. 2.China Academy of Information and Communications TechnologyBeijingChina

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