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Achieving privacy protection for crowdsourcing application in edge-assistant vehicular networking

Author

Listed:
  • Hui Li

    (University of Electronic Science and Technology of China
    University of Electronic Science and Technology of China)

  • Lishuang Pei

    (University of Electronic Science and Technology of China)

  • Dan Liao

    (University of Electronic Science and Technology of China
    University of Electronic Science and Technology of China)

  • Ming Zhang

    (University of Electronic Science and Technology of China
    University of Electronic Science and Technology of China)

  • Du Xu

    (University of Electronic Science and Technology of China)

  • Xiong Wang

    (University of Electronic Science and Technology of China)

Abstract

Crowdsourcing application, deemed as a key evolution on the way to vehicular networking, has great potential to provide real-time services. However, existing cloud-based vehicular networking cannot support real-time data transmission with wasting massive bandwidth resources. This paper studies the crowdsourcing application in edge-assistant vehicular networking. To improve the real-time demand of data transmission, we propose the E-node of that owns the learning and semantic analysis abilities. Then we analyze two data transmission scenarios of crowdsourcing for collected data: road map uploading, traffic accident and traffic flow. On the other hand, to address the privacy leakages in the process of data aggregation and data distribution, we separately design time-tolerance anonymous privacy protection algorithm and k − 1 location-offset privacy protection algorithm. Finally, we conduct extensive experiments to verify the effectiveness of our proposed privacy protection algorithms, including time delay, offset probability, privacy leakage probability and accuracy.

Suggested Citation

  • Hui Li & Lishuang Pei & Dan Liao & Ming Zhang & Du Xu & Xiong Wang, 2020. "Achieving privacy protection for crowdsourcing application in edge-assistant vehicular networking," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 75(1), pages 1-14, September.
    • Handle: RePEc:spr:telsys:v:75:y:2020:i:1:d:10.1007_s11235-020-00666-w
    DOI: 10.1007/s11235-020-00666-w
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    References listed on IDEAS

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    1. Dingde Jiang & Liuwei Huo & Ya Li, 2018. "Fine-granularity inference and estimations to network traffic for SDN," PLOS ONE, Public Library of Science, vol. 13(5), pages 1-23, May.
    2. Jianan Sun & Qing Gu & Tao Zheng & Ping Dong & Yajuan Qin, 2019. "Joint communication and computing resource allocation in vehicular edge computing," International Journal of Distributed Sensor Networks, , vol. 15(3), pages 15501477198, March.
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