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ECAS: an efficient and conditional privacy preserving collision warning system in fog-based vehicular ad hoc networks

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CCF Transactions on Networking

Abstract

VANET is a prominent way to provide road security and prevent vehicles from collision by using various methods, such as message dissemination, traffic management, etc. However, the traditional vehicular network faces some problems related to high latency, low bandwidth, and communication in open wireless environment. Thus, some researchers have attempted to combine fog computing with vehicular ad hoc networks to overcome these problems. In this paper, we design an efficient and conditional privacy preserving collision warning system for fog-based vehicular ad hoc networks without using bilinear pairing. The fog nodes collect the speed violation reports from the speed sensor of vehicles. This protocol achieves privacy protection, message authentication, and revocate malicious vehicles. We also provide strict security proof and illustrate how to reach the security requirements in the proposed protocol. Moreover, the experiment demonstrates that the proposed protocol provides better efficiency in computation overhead and communication overhead, and makes it more applicable for adoption in the VANET collision warning systems.

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Acknowledgements

Funding was provided by National Science Foundation of China (Grant nos. 61872059, 61502085).

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Authors and Affiliations

  1. Network and Data Security Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 611731, China

    Zhiguang Qin, Yuedi Li, Xin Ye, Jin Zhou, Minsheng Cao & Dajiang Chen

  2. Peng Cheng Laboratory, Shenzhen, 518055, China

    Dajiang Chen

Authors
  1. Zhiguang Qin
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  2. Yuedi Li
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  3. Xin Ye
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  4. Jin Zhou
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  5. Minsheng Cao
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  6. Dajiang Chen
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Correspondence to Dajiang Chen.

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Qin, Z., Li, Y., Ye, X. et al. ECAS: an efficient and conditional privacy preserving collision warning system in fog-based vehicular ad hoc networks. CCF Trans. Netw. 3, 205–217 (2020). https://doi.org/10.1007/s42045-020-00041-y

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  • DOI: https://doi.org/10.1007/s42045-020-00041-y

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