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An Efficient Privacy Preserving Batch Authentication Scheme with Deterable Function for VANETs

  • Jinhui Liu
  • Yong YuEmail author
  • Yanqi Zhao
  • Jianwei Jia
  • Shijia Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11058)

Abstract

With the rapid development of Internet of Things (IoT), intelligent transportation systems (ITS) brings more and more intelligent and convenient services to people’s daily lives. Vehicular Ad hoc Networks (VANETs), as a typical application of ITS, is becoming an effective approach to manage traffic systems. However, VANETs still have to face to different security challenges in practice. In this paper, we develop a new identity-based double authentication preventing signature using bilinear pairings and then adopt it to propose a novel privacy preserving authentication scheme with deterable function based on our proposed identity-based double authentication preventing signature. This scheme provides secure authentication process for messages transmitted between vehicles and RSUs. A batch message verification mechanism is also supported by the proposed scheme to increase the message processing throughput of RSUs. And the security proof and performance analysis are presented. By comparing with other IBCPPA scheme in terms of the total time of pseudo identity generation and message signing phase and the execution times of batch verification, our proposed scheme is more efficient.

Keywords

Vehicular ad-hoc networks Authentication protocol Double authentication preventing signature Bilinear pairing Elliptic curve 

Notes

Acknowledgement

This work was supported by the National Key R&D Program of China (2017YFB0802000), the National Natural Science Foundation of China (61772326, 61572303, 61872229, 61802239), NSFC Research Fund for International Young Scientists (61750110528), National Cryptography Development Fund during the 13th Five-year Plan Period (MMJJ20170216), Fundamental Research Funds for the Central Universities (GK201702004, GK201803061, 2018CBLY006) and China Postdoctoral Science Foundation (2018M631121).

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Jinhui Liu
    • 1
  • Yong Yu
    • 1
    Email author
  • Yanqi Zhao
    • 1
  • Jianwei Jia
    • 2
  • Shijia Wang
    • 3
  1. 1.School of Computer ScienceShaanxi Normal UniversityXi’anChina
  2. 2.Computer School of Wuhan UniversityWuhanChina
  3. 3.Department of Statistics and Actuarial ScienceSimon Fraser UniversityBurnabyCanada

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