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Intelligent Adversary Placements for Privacy Evaluation in VANET

School of Computer Science, University of Windsor, Windsor, ON N9B 3P4, Canada
Author to whom correspondence should be addressed.
Information 2020, 11(9), 443;
Received: 16 July 2020 / Revised: 4 September 2020 / Accepted: 7 September 2020 / Published: 14 September 2020
(This article belongs to the Special Issue Vehicle-To-Everything (V2X) Communication)
Safety applications in Vehicular Ad-hoc Networks (VANETs) often require vehicles to share information such as current position, speed, and vehicle status on a regular basis. This information can be collected to obtain private information about vehicles/drivers, such as home or office locations and frequently visited places, creating serious privacy vulnerabilities. The use of pseudonyms, rather than actual vehicle IDs, can alleviate this problem and several different Pseudonym Management Techniques (PMTs) have been proposed in the literature. These PMTs are typically evaluated assuming a random placement of attacking stations. However, an adversary can utilize knowledge of traffic patterns and PMTs to place eavesdropping stations in a more targeted manner, leading to an increased tracking success rate. In this paper, we propose two new adversary placement strategies and study the impact of intelligent adversary placement on tracking success using different PMTs. The results indicate that targeted placement of attacking stations, based on traffic patterns, road type, and knowledge of PMT used, can significantly increase tracking success. Therefore, it is important to take this into consideration when developing PMTs that can protect vehicle privacy even in the presence of targeted placement techniques. View Full-Text
Keywords: location privacy; attack modeling; V2V communication; DSRC; vehicle-to-everything location privacy; attack modeling; V2V communication; DSRC; vehicle-to-everything
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MDPI and ACS Style

Saini, I.; St. Amour, B.; Jaekel, A. Intelligent Adversary Placements for Privacy Evaluation in VANET. Information 2020, 11, 443.

AMA Style

Saini I, St. Amour B, Jaekel A. Intelligent Adversary Placements for Privacy Evaluation in VANET. Information. 2020; 11(9):443.

Chicago/Turabian Style

Saini, Ikjot, Benjamin St. Amour, and Arunita Jaekel. 2020. "Intelligent Adversary Placements for Privacy Evaluation in VANET" Information 11, no. 9: 443.

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