TrafficAdaptor: an adaptive obfuscation strategy for vehicle location privacy against traffic flow aware attacks

Chenxi Qiu, Li Yan, Anna Squicciarini, Juanjuan Zhao, Chengzhong Xu, Primal Pappachan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

One of the most popular location privacy-preserving mechanisms applied in location-based services (LBS) is location obfuscation, where mobile users are allowed to report obfuscated locations instead of their real locations to services. Many existing obfuscation approaches consider mobile users that can move freely over a region. However, this is inadequate for protecting the location privacy of vehicles, as their mobility is restricted by external factors, such as road networks and traffic flows. This auxiliary information about external factors helps an attacker to shrink the search range of vehicles' locations, increasing the risk of location exposure. In this paper, we propose a vehicle traffic flow aware attack that leverages public traffic flow information to recover a vehicle's real location from obfuscated location. As a countermeasure, we then develop an adaptive strategy to obfuscate a vehicle's location by a "fake"trajectory that follows a realistic traffic flow. The fake trajectory is designed to not only hide the vehicle's real location but also guarantee the quality of service (QoS) of LBS. Our experimental results demonstrate that 1) the new threat model can accurately track vehicles' real locations, which have been obfuscated by two state-of-the-art algorithms, and 2) the proposed obfuscation method can effectively protect vehicles' location privacy under the new threat model without compromising QoS.

Original languageEnglish (US)
Title of host publication30th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2022
EditorsMatthias Renz, Mohamed Sarwat, Mario A. Nascimento, Shashi Shekhar, Xing Xie
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781450395298
DOIs
StatePublished - Nov 1 2022
Event30th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, SIGSPATIAL GIS 2022 - Seattle, United States
Duration: Nov 1 2022Nov 4 2022

Publication series

NameGIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems

Conference

Conference30th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, SIGSPATIAL GIS 2022
Country/TerritoryUnited States
CitySeattle
Period11/1/2211/4/22

All Science Journal Classification (ASJC) codes

  • Earth-Surface Processes
  • Computer Science Applications
  • Modeling and Simulation
  • Computer Graphics and Computer-Aided Design
  • Information Systems

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