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Scalable privacy-enhanced traffic monitoring in vehicular ad hoc networks

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Abstract

Vehicular ad hoc network (VANET) has been proposed to improve traffic safety and driving experience. This is realized by equipping vehicles with capacity to talk to each other. Vehicular communication security and privacy are viewed as a major obstacle to widely deploy VANET. Most existing works devote to the authenticity and identity anonymity of vehicle-to-vehicle (V2V) communications. In this paper, we investigate secure vehicle-to-infrastructure (V2I) communications for applications in which a traffic monitoring center collects information about the traffic and road status through VANET. We propose a scalable privacy-enhanced traffic monitoring (SPETM) framework. In this framework, the traffic monitoring center can collect authenticated individual vehicle driving status and the road usage information, with the help of the distributed roadside units (RSUs) who can simultaneously obtain local view of the traffic in their respective domains. Neither external attackers, malicious vehicles nor compromised RSUs can know the identity of the reporting vehicles, which guarantees enhanced privacy for vehicles. However, misbehaving vehicles abusing the privacy mechanisms can be jointly traced by the semi-trusted vehicle management authorities, which provides desirable liability in privacy-preserving vehicular reports. We instantiate a concrete SPETM scheme with well-established cryptographic technologies. Thorough analyses show that our scheme is secure and practical for deployment.

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Acknowledgments

This paper is partially supported by the National Key Basic Research Program (973 program) under Project 2012CB315905, by the Natural Science Foundation under Projects 61370190, 61272501, 61173154, 61402029 and 61003214, by the Beijing Natural Science Foundation through Projects 4132056 and 4122041, by the Natural Science Foundation of Guangdong Province through Projects 2014A030313517 and S2012020011071, the Fundamental Research Funds for the Central Universities, and the Research Funds (No. 14XNLF02) of Renmin University of China, the Open Research Fund of the State Key Laboratory of Information Security, Open Project of Key Laboratory of Cryptologic Technology and Information Security, Ministry of Education, Shandong University (Institute of Information Engineering, Chinese Academy of Sciences and the Open Research Fund of Beijing Key Laboratory of Trusted Computing.

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Authors and Affiliations

  1. School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, China

    Yi Liu & Jie Ling

  2. The State Key Laboratory of Integrated Services Networks, Xidian University, Xi’an, China

    Yi Liu

  3. The State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences, Beijing, 100093, China

    Yi Liu

  4. School of Electronics and Information Engineering, Beihang University, XueYuan Road No. 37, Haidian District, Beijing, China

    Qianhong Wu

  5. The Key Laboratory of Data Engineering and Knowledge Engineering (Renmin University of China) Ministry of Education, School of Information, Renmin University of China, ZhongGuanCun Street No. 59, Haidian District, Beijing, China

    Bo Qin

  6. Key Laboratory of Cryptologic Technology and Information Security, Ministry of Education, Shandong University, Shandong, China

    Qianhong Wu

Authors
  1. Yi Liu
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  2. Jie Ling
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  3. Qianhong Wu
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  4. Bo Qin
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Corresponding author

Correspondence to Qianhong Wu.

Additional information

Communicated by V. Loia.

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Liu, Y., Ling, J., Wu, Q. et al. Scalable privacy-enhanced traffic monitoring in vehicular ad hoc networks. Soft Comput 20, 3335–3346 (2016). https://doi.org/10.1007/s00500-015-1737-y

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