A survey of local differential privacy for securing internet of vehicles
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A survey of local differential privacy for securing internet of vehicles Ping Zhao1 · Guanglin Zhang1 · Shaohua Wan2 · Gaoyang Liu3 · Tariq Umer4
© Springer Science+Business Media, LLC, part of Springer Nature 2019
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. Keywords Internet of connected vehicles (IoV) · Data privacy · Local differential privacy (LDP) · Differential privacy (DP)
1 Introduction Internet of connected vehicles (IoV) will significantly facilitate many applications, e.g., intelligent traffic management, intelligent dynamic information services, intelligent vehicle control, etc., since 5.2 million vehicles are on world’s roads by 2017 [1, * Shaohua Wan [email protected]; [email protected] Extended author information available on the last page of the article
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Fig. 1 Illustration of internet of connected vehicles (IoV) and the risk of disclosing data privacy
2] and the number of vehicles is expected to be doubled by 2040 [3]. The potential data disclosure of such a large number of unsecured vehicles is the bottleneck of the application and development of IoV. Therefore, the data privacy issues in IoV have caused a wide range of attentions. IoV definitely facilitates human’s life, but such benefits come with a huge price of data privacy [4, 5]. As shown in Fig. 1, vehicles in IoV are connected with each other and request various services via sharing data with other vehicles and servers. But it incurs potential risk of disclosing data privacy of vehicles to attackers, since
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