A Geo-Indistinguishable Context-Based Mix Strategy for Trajectory Protection in VANETs
Vehicular Ad-Hoc Networks (VANETs), the backbone of Intelligent Transportation Systems (ITS), can significantly improve traffic efficiency and road safety. However, preserving privacy during data transmission and storage is a significant issue in vehicular communications. To prevent malicious adversaries from tracking vehicles from the beacons they broadcast, pseudonym-changing or obfuscation strategies have been proposed in literature. However, it is challenging to protect the privacy of vehicles while simultaneously maintaining the quality of service of location-based services. In this article, the authors present a new context-based mix strategy that provides a higher privacy level and reduces information loss during the pseudonym-changing process. A global passive adversary model is developed to evaluate its performance and the effectiveness of the proposed mechanism is demonstrated. Finally, a uniform framework to evaluate different strategies for vehicular privacy is developed and used to compare their strengths and weaknesses in different scenarios.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/00189545
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Supplemental Notes:
- Copyright © 2023, IEEE.
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Authors:
- Zhang, Zhixiang
- Feng, Tianyi
- Wong, Wai-Choong
- Sikdar, Biplab
- Publication Date: 2023-12
Language
- English
Media Info
- Media Type: Web
- Features: References;
- Pagination: pp 16538-16552
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Serial:
- IEEE Transactions on Vehicular Technology
- Volume: 72
- Issue Number: 12
- Publisher: Institute of Electrical and Electronics Engineers (IEEE)
- ISSN: 0018-9545
- Serial URL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=25
Subject/Index Terms
- TRT Terms: Computer security; Data privacy; Highway safety; Vehicle trajectories; Vehicular ad hoc networks
- Subject Areas: Data and Information Technology; Highways; Vehicles and Equipment;
Filing Info
- Accession Number: 01903388
- Record Type: Publication
- Files: TRIS
- Created Date: Dec 27 2023 10:28AM