Challenges and Opportunities of Privacy-Preserving Computation Techniques in IoV Edge Services: A Systematic Review and Meta-Analysis

The evolution of Internet of Vehicles (IoV) technologies, encompassing wireless communications and Artificial Intelligence (AI), has advanced the collaborative “Pedestrian-Vehicle-Road-Cloud” IoV edge services, enhancing road efficiency and driving safety. Operating in an open-edge environment with vast sensory data, IoV faces significant privacy risks from unauthorized access and data breaches. Consequently, privacy-preserving computation (PPC) is crucial for secure IoV services. This paper reviews PPC techniques in IoV edge services, exploring network characteristics and potential privacy attacks. It categorizes and evaluates techniques such as differential privacy, homomorphic encryption, and secure multi-party computation based on data security, utility, and overhead. Summarizing their pros and cons, the challenges and future research directions for IoV edge services are outlined.