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

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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.

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References

  1. Yang, K., Menendez, M., & Guler, S. (2019). Implementing transit signal priority in a connected vehicle environment with and without bus stops. Transportmetrica B-Transport Dynamics,7(1), 423–445.

    Google Scholar 

  2. Automotive Industry Trends. (2019). IoT connected smart cars vehicles. http://www.businessinsider.com/internet-of-things-connected-smart-cars. Accessed 10 Nov 2019.

  3. Rodrigues, F., Henrickson, K., & Pereira, F. (2019). Multi-output gaussian processes for crowdsourced traffic data imputation. IEEE Transactions on Intelligent Transportation Systems,20(2), 594–603.

    Google Scholar 

  4. He, Z., Cao, J., Liu, X. (2015). High quality participant recruitment in vehicle-based crowdsourcing using predictable mobility. In INFCOM (pp. 2542–2559).

  5. Zhang, P., Hu, C., Chen, D., et al. (2018). ShiftRoute: achieving location privacy for map services on smartphones. IEEE Transactions on Vehicular Technology,67(5), 4527–4538.

    Google Scholar 

  6. Jiang, D., Wang, W., Shi, L., et al. (2018). A compressive sensing-based approach to end-to-end network traffic reconstruction. IEEE Transactions on Network Science and Engineering,5(3), 1–12.

    Google Scholar 

  7. Feng, X., & Wang, L. (2019). PAU: Privacy assessment method with uncertainty consideration for cloud-based vehicular networks. Future Generation Computer Systems,96, 368–375.

    Google Scholar 

  8. Md, W., Mehdi, S., Abdullah, G., et al. (2014). A survey on vehicular cloud computing. Journal of Network and Computer Applications,40, 325–344.

    Google Scholar 

  9. Lee, S., Lee, J., & Cho, H. (2018). A study of mobile edge computing system architecture for connected car media services on highway. KSII Transactions on Internet and Information Systems,12(12), 5669–5684.

    Google Scholar 

  10. Jiang, D., Huo, L., Lv, Z., et al. (2018). A joint multi-criteria utility-based network selection approach for vehicle-to-infrastructure networking. IEEE Transactions on Intelligent Transportation Systems,19(10), 3305–3319.

    Google Scholar 

  11. Zhou, L., Yu, L., Du, S., et al. (2019). Achieving differentially private location privacy in edge-assistant connected vehicles. IEEE Internet of Things Journal,6, 1–10.

    Google Scholar 

  12. Jiang, D., Wang, Y., Lv, Z., et al. (2020). Big data analysis-based network behavior insight of cellular networks for industry 4.0 applications. IEEE Transactions on Industrial Informatics, 16(2), 1–9.

    Google Scholar 

  13. Jiang, D., Huo, L., & Song, H. (2018). Rethinking behaviors and activities of base stations in mobile cellular networks based on big data analysis. IEEE Transactions on Network Science and Engineering,1(1), 1–12.

    Google Scholar 

  14. Le, T., & Hu, R. (2018). Mobility-aware edge caching and computing in vehicle networks: a deep reinforcement learning. IEEE Transactions on Vehicular Technology,67(11), 10190–10203.

    Google Scholar 

  15. Feng, J., Liu, Z., Wu, C., et al. (2017). AVE: Autonomous vehicular edge computing framework with ACO-based scheduling. IEEE Transactions on Vehicular Technology,66(12), 10660–10675.

    Google Scholar 

  16. Gu, B., Chen, Y., Liao, H., et al. (2018). A distributed and context-aware task assignment mechanism for collaborative mobile edge computing. Sensors,18(8), 1–17.

    Google Scholar 

  17. Huang, C., Chiang, M., Dao, D., et al. (2018). V2V data offloading for cellular network based on the software defined network (SDN) inside mobile edge computing (MEC) architecture. IEEE Access,6, 17741–17755.

    Google Scholar 

  18. Jiang, D., Huo, L., & Li, Y. (2018). Fine-granularity inference and estimations to network traffic for SDN. PLoS ONE,13(5), 1–23.

    Google Scholar 

  19. Sun, J., Gu, Q., Zheng, T., et al. (2019). Joint communication and computing resource allocation in vehicular edge computing. International Journal of Distributed Sensor Networks,15(3), 1–13.

    Google Scholar 

  20. Nguyen, T., Nguyen, V., Pham, A., et al. (2018). Cost-effective resource sharing in an internet of vehicles-employed mobile edge computing environment. Symmetry,10(11), 1–19.

    Google Scholar 

  21. Shailsh, C., Naik, R., & Jose, J. (2019). Crowdsourcing-based traffic simulation for smart freight mobility. Simulation Modelling Practice and Theory,95, 1–15.

    Google Scholar 

  22. Ali, K., Al-Yaseen, D., Ejaz, A., et al. (2012). CrowdITS: Crowdsourcing in intelligent transportation systems. In Wireless communications and networking conference (WCNC) (pp. 3307–3311).

  23. Misra, A., Gooze, A., Watkins, K., et al. (2014). Crowdsourcing and its application to transportation data collection and management. Transportation Research Record,2414, 1–8.

    Google Scholar 

  24. Metlo, S., Memon, M., Shaikh, F., et al. (2019). Crowdsource based vehicle tracking system. Wireless Personal Communications,106(4), 2387–2405.

    Google Scholar 

  25. Andrew, F., Kumar, B., Chen, J., et al. (2017). Multi-lane pothole detection from crowdsourced undersampled vehicle sensor data. IEEE Transactions on Mobile Computing,16(12), 3417–3430.

    Google Scholar 

  26. Gurdit, S., Divya, B., & Sanieev, S. (2017). Smart patrolling: An efficient road surface monitoring using smartphone sensors and crowdsourcing. Pervasive and Mobile Computing,40, 71–88.

    Google Scholar 

  27. Yang, X., Tang, U., Niu, L., et al. (2018). Generating lane-based intersection maps from crowdsourcing big trace data. Transportation Research Part C-Emerging Technologies,89, 168–187.

    Google Scholar 

  28. Kang, J., Yu, R., Huang, X., et al. (2018). Privacy-preserved pseudonym scheme for fog computing supported internet of vehicles. IEEE Transactions on Intelligent Transportation Systems,19(8), 2627–2637.

    Google Scholar 

  29. Wei, J., Wang, X., Li, N., et al. (2018). A privacy-preserving fog computing framework for vehicular crowdsensing networks. IEEE Access,6, 43776–43784.

    Google Scholar 

  30. Ma, L., Liu, X., Pei, Q., et al. (2019). Privacy-preserving reputation management for edge computing enhanced mobile crowdsensing. IEEE Transactions on Services Computing,12(5), 786–799.

    Google Scholar 

  31. Basudan, S., Lin, X., & Sankaranarayanan, K. (2017). A privacy-preserving vehicular crowdsensing-based road surface condition monitoring system using fog computing. IEEE Internet of Things Journal,4(3), 772–782.

    Google Scholar 

  32. Li, M., Zhu, L., & Lin, X. (2019). Privacy-preserving traffic monitoring with false report filtering via fog-assisted vehicular crowdsensing. IEEE Transaction on Services Computing, 99, 1–11.

    Google Scholar 

  33. Onieva, J., Rios, R., Roman, R., et al. (2019). Edge-assisted vehicular networks security. IEEE Internet of Things Journal,68(5), 8038–8045.

    Google Scholar 

  34. Li, L., & Wang, F. (2010). Probability and mathematical statistics (pp. 26–34). Beijing: Chemical Industry Press.

    Google Scholar 

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Acknowledgements

This research was partially supported by the Project on Public Safety Risk Prevention and Control and Emergency Technical Equipment (2018YFC0831002), Sichuan science and technology program (2019YFG0206), National Natural Science Foundation of China (61971105), Fundamental Research Funds for the Central Universities (ZYGX2019J004, ZYGX2019J125).

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Authors and Affiliations

  1. Key Lab of Optical Fiber Sensing and Communications (Ministry of Education), University of Electronic Science and Technology of China, No. 2006 Xiyuan Road, hi-tech West District, Chengdu, Sichuan, China

    Hui Li, Lishuang Pei, Dan Liao, Ming Zhang, Du Xu & Xiong Wang

  2. Chengdu Research Institute, University of Electronic Science and Technology of China, Chengdu, China

    Hui Li, Dan Liao & Ming Zhang

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  1. Hui Li
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  2. Lishuang Pei
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  3. Dan Liao
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  4. Ming Zhang
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  5. Du Xu
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  6. Xiong Wang
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Correspondence to Ming Zhang.

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Li, H., Pei, L., Liao, D. et al. Achieving privacy protection for crowdsourcing application in edge-assistant vehicular networking. Telecommun Syst 75, 1–14 (2020). https://doi.org/10.1007/s11235-020-00666-w

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