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Cited by 478

The DOI matches its Crossref record; 77 of the 77 references it cites were found in a registry or an index.

A Security and Privacy Review of VANETs

Qu, Fengzhong, Wu, Zhihui, Wang, Feiyue, Cho, Woong·2015·IEEE Transactions on Intelligent Transportation Systems

Abstract

Vehicular ad hoc networks (VANETs) have stimulated interest in both academic and industry settings because, once deployed, they would bring a new driving experience to drivers. However, communicating in an open-access environment makes security and privacy issues a real challenge, which may affect the large-scale deployment of VANETs. Researchers have proposed many solutions to these issues. We start this paper by providing background information of VANETs and classifying security threats that challenge VANETs. After clarifying the requirements that the proposed solutions to security and privacy problems in VANETs should meet, on the one hand, we present the general secure process and point out authentication methods involved in these processes. Detailed survey of these authentication algorithms followed by discussions comes afterward. On the other hand, privacy preserving methods are reviewed, and the tradeoff between security and privacy is discussed. Finally, we provide an outlook on how to detect and revoke malicious nodes more efficiently and challenges that have yet been solved.

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Metadata

Authors
Qu, F., Wu, Z., Wang, F., & Cho, W.
Venue
IEEE Transactions on Intelligent Transportation Systems
Published
2015-12
Volume
16
Issue
6
Pages
2985-2996
ISSN
  • 1524-9050 (ISSN-L)
  • 1558-0016
OpenAlex
W2180055784
Cited by
478
References
77
FWCI
14.27
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: A Security and Privacy Review of VANETs. https://app.cochat.ai/papers/10.1109/tits.2015.2439292
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Qu, F., Wu, Z., Wang, F., & Cho, W. (2015). A Security and Privacy Review of VANETs. IEEE Transactions on Intelligent Transportation Systems. https://doi.org/10.1109/tits.2015.2439292

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.