Protecting Privacy of Location-Based Services in Road Networks

Zheng Tan, Cheng Wang, Chungang Yan, Mengchu Zhou, Changjun Jiang

Research output: Contribution to journalArticlepeer-review

18 Scopus citations


Location-Based Services (LBS), which answer users' location-dependent queries to Points of Interest (POI), have become popular along with mobile devices' widespread use. While benefiting from convenience, users may suffer privacy leak risk, due to publishing sensitive information, i.e., locations, to servers. Previous studies have proposed a number of methods for protecting LBS. Through providing provable privacy protection, Private Information Retrieval (PIR) becomes well-known for its high effectiveness in protecting privacy. However, the PIR-protected LBS must cautiously handle the consequent transmission/computation cost and service error brought by adopting PIR, as both of them impact user experience. Then an important question arises: Can the user experience of PIR-protected LBS be greatly improved? We answer it by presenting a novel interface from PIR to LBS, gaining benefits from road networks. The proposed interface is comprised of two road partition methods fully considering POI's distribution along roads, and two response-calculating methods to satisfy users' requirements on driving distance. Since Vehicular Location Based Services contain adequate road network knowledge, we accomplish our work on them. The experimental results on a real dataset validate that our interface can improve PIR-protected LBS greatly in user experience while protecting privacy.

Original languageEnglish (US)
Pages (from-to)6435-6448
Number of pages14
JournalIEEE Transactions on Intelligent Transportation Systems
Issue number10
StatePublished - Oct 1 2021

All Science Journal Classification (ASJC) codes

  • Automotive Engineering
  • Mechanical Engineering
  • Computer Science Applications


  • Internet of Vehicles
  • Location-Based Services (LBS)
  • Points of Interest (POI)
  • computational private information retrieval (cPIR)
  • query privacy


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