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A survey of local differential privacy for securing internet of vehicles

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Abstract

Internet of connected vehicles (IoV) are expected to enable intelligent traffic management, intelligent dynamic information services, intelligent vehicle control, etc. However, vehicles’ data privacy is argued to be a major barrier toward the application and development of IoV, thus causing a wide range of attentions. Local differential privacy (LDP) is the relaxed version of the privacy standard, differential privacy, and it can protect users’ data privacy against the untrusted third party in the worst adversarial setting. Therefore, LDP is potential to protect vehicles’ data privacy in the practical scenario, IoV, although vehicles exhibit unique features, e.g., high mobility, short connection times, etc. To this end, in this paper, we first give an overview of the existing LDP techniques and present the thorough comparisons of these work in terms of advantages, disadvantages, and computation cost, in order to get the readers well acquainted with LDP. Thereafter, we investigate the potential applications of LDP in securing IoV in detail. Last, we direct several future research directions of LDP in IoV, to bridge the gaps between LDP researches and the privacy preservation in IoV. The originality of this survey is that it is the first work to summarize and compare the existing LDP research work and that it also does an pioneering work toward the in-depth analysis of the potential applications of LDP in privacy preservation in IoV.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant 61902060, part by Shanghai Sailing Program 19YF1402100, part by the Fundamental Research Funds for the Central Universities 2232019D3-51, 2722019PY052, and part by “Chenguang Program” supported by Shanghai Education Development Foundation and Shanghai Municipal Education Commission.

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  1. College of Information Science and Technology, Donghua University, Shanghai, 201620, China

    Ping Zhao & Guanglin Zhang

  2. School of Information and Safety Engineering, Zhongnan University of Economics and Law, Wuhan, 430073, China

    Shaohua Wan

  3. School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China

    Gaoyang Liu

  4. Department of Computer Science, COMSATS University Islamabad, Lahore Campus, Lahore, Pakistan

    Tariq Umer

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  1. Ping Zhao
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Zhao, P., Zhang, G., Wan, S. et al. A survey of local differential privacy for securing internet of vehicles. J Supercomput 76, 8391–8412 (2020). https://doi.org/10.1007/s11227-019-03104-0

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