Toward Privacy Protection Composition Framework on Internet of Vehicles

As an emerging computing paradigm, the Internet of Vehicles (IoV) brings a lot of benefits for drivers and consumers, such as route recommendation, reducing traffic congestion, and parking difficulty. However, participants in IoV are inevitably exposed to privacy threats because their data is outsourced to a third party. This paper presents a comprehensive analysis of the root causes of privacy risks in IoV and the potential solutions. We begin by introducing the concrete privacy challenges, which are tied to the structural properties of IoV. Then, we present a unified framework of data privacy preservation in IoV, which integrates multiple privacy techniques, including encryption, anonymity, and perturbation. Next, we investigate the possible research directions for route recommendation, charging deployment, traffic prediction under different privacy requirements in detail. Finally, we discuss emerging techniques and environments (e.g., blockchain, smart grid, 5G communication) to strengthen the ability and application of IoV under strong protection. With the fast development of communication and infrastructure technologies, the Internet of Vehicles (IoV) is attracting more and more attention from academia and industry [1]. The novel computing paradigm consists of hundreds of thousands of interacted and autonomous vehicles. Each is viewed as an intelligent object capable of sensing, computing, and data storage. Intelligent entities are able to undertake multiple different roles, which brings a series of functions, including autonomous driving and platooning, information sharing and learning, traffic control, and optimization.