A Distributed Mix-Context-Based Method for Location Privacy in Road Networks

Ikram Ullah, Munam Ali Shah, Abid Khan, Carsten Maple, Abdul Waheed and Gwnaggil Jeon
Additional contact information
Ikram Ullah: Department of Computer Science, COMSATS University Islamabad, Islamabad 45550, Pakistan
Munam Ali Shah: Department of Computer Science, COMSATS University Islamabad, Islamabad 45550, Pakistan
Abid Khan: Department of Computer Science, School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough TS1 3BX, UK
Carsten Maple: Secure Cyber Systems Research Group, WMG, University of Warwick, Coventry CV4 7AL, UK
Abdul Waheed: Department of Computer Science, COMSATS University Islamabad, Islamabad 45550, Pakistan
Gwnaggil Jeon: Department of Embedded Systems Engineering, Incheon National University, 119 Academy-ro, Yeonsugu, Incheon 22012, Korea

Sustainability, 2021, vol. 13, issue 22, 1-32

Abstract: Preserving location privacy is increasingly an essential concern in Vehicular Adhoc Networks (VANETs). Vehicles broadcast beacon messages in an open form that contains information including vehicle identity, speed, location, and other headings. An adversary may track the various locations visited by a vehicle using sensitive information transmitted in beacons such as vehicle identity and location. By matching the vehicle identity used in beacon messages at various locations, an adversary learns the location history of a vehicle. This compromises the privacy of the vehicle driver. In existing research work, pseudonyms are used in place of the actual vehicle identity in the beacons. Pseudonyms should be changed regularly to safeguard the location privacy of vehicles. However, applying simple change in pseudonyms does not always provide location privacy. Existing schemes based on mix zones operate efficiently in higher traffic environments but fail to provide privacy in lower vehicle traffic densities. In this paper, we take the problem of location privacy in diverse vehicle traffic densities. We propose a new Crowd-based Mix Context (CMC) privacy scheme that provides location privacy as well as identity protection in various vehicle traffic densities. The pseudonym changing process utilizes context information of road such as speed, direction and the number of neighbors in transmission range for the anonymisation of vehicles, adaptively updating pseudonyms based on the number of a vehicle neighbors in the vicinity. We conduct formal modeling and specification of the proposed scheme using High-Level Petri Nets (HPLN). Simulation results validate the effectiveness of CMC in terms of location anonymisation, the probability of vehicle traceability, computation time (cost) and effect on vehicular applications.

Keywords: anonymity; formal modeling; location privacy; mix context; pseudonyms; traceability; VANETs (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
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