Adaptive and Reliable Location Privacy Risk Sensing in Internet of Vehicles

The Internet of Vehicles (IoV) is a large-scale interactive network that operates in a dynamic and changeable environment, encompassing diverse types of private information. In recent years, safeguarding vehicle location privacy in IoV has been a topic of concern. However, the independent location privacy protection mechanisms cannot consistently meet the rigorous security requirements of various IoV scenarios, which will pose a significant threat to the location privacy of IoV users. In contrast, risk sensing as a preventive security strategy needs lower computing costs and is more suitable for the intricacies of complex city environments. Unfortunately, the existing works lack that combination of risk assessment with trust assessment to conduct a comprehensive study on location privacy risk sensing. Therefore, considering the city vehicles' spatial clustering phenomenon and the strong regularity of traffic flow, we propose a risk-sensing approach to vehicle location privacy based on the continuous adaptive risk and trust assessment strategy. This approach employs the Ripley method to analyze space clustering characteristics and combines the traffic flow prediction model to establish the risk assessment scheme. Furthermore, to enable our risk-sensing approach to have historical memory that can identify and continuously track malicious users, we incorporate a penalty factor into the trust assessment scheme that updates in a time iterative format. Extensive numerical results demonstrate the adaptability and reliability of our proposed risk-sensing approach to vehicle location privacy.