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Scalable privacy-enhanced traffic monitoring in vehicular ad hoc networks

  • Yi Liu
  • , Jie Ling
  • , Qianhong Wu*
  • , Bo Qin
  • *Corresponding author for this work
  • Guangdong University of Technology
  • Xidian University
  • CAS - Institute of Information Engineering
  • Shandong University
  • Renmin University of China

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Pages (from-to)3335-3346
Number of pages12
JournalSoft Computing
Volume20
Issue number8
DOIs
StatePublished - 1 Aug 2016

Keywords

  • Spatio-temporal identity
  • Traffic monitoring
  • Vehicle ad hoc network
  • Vehicle privacy

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