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The Journal of Supercomputing
Erschienen in: The Journal of Supercomputing 11/2020

09.12.2019

A survey of local differential privacy for securing internet of vehicles

verfasst von: Ping Zhao, Guanglin Zhang, Shaohua Wan, Gaoyang Liu, Tariq Umer

Erschienen in: The Journal of Supercomputing | Ausgabe 11/2020

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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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Metadaten
Titel
A survey of local differential privacy for securing internet of vehicles
verfasst von
Ping Zhao
Guanglin Zhang
Shaohua Wan
Gaoyang Liu
Tariq Umer
Publikationsdatum
09.12.2019
Verlag
Springer US
Erschienen in
The Journal of Supercomputing / Ausgabe 11/2020
Print ISSN: 0920-8542
Elektronische ISSN: 1573-0484
DOI
https://doi.org/10.1007/s11227-019-03104-0

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