A Survey of the Social Internet of Vehicles: Secure Data Issues, Solutions, and Federated Learning
Ling Xing, Pengcheng Zhao, Jianping Gao, Honghai Wu, Huahong Ma
Abstract
The Social Internet of Vehicles (SIoV) is constructed so that the vehicle, as an entity, generates social awareness, which leads to the social behavior of the vehicle. The SIoV has multidimensional, heterogeneous, massive, real-time dynamic data, so its data security is a major concern. The structure of the SIoV and the characteristics of security primitives are studied in this review, which defines the social community by the vehicle. After this, the attack model is elaborated on, further refining the source, target, and capabilities of the attack. On this basis, typical data security solutions are compared and analyzed. In this survey, the focus is on the collaborative application of federated learning in SIoV data security protection. Ultimately, the current challenges and possible future research directions in this field are discussed.