Preserving privacy in Internet of Vehicles (IoV): a novel group-leader-based shadowing scheme using blockchain

Recent developments in the Internet of Vehicles (IoV) and vehicular adhoc networks (VANET) have revolutionized our infrastructure, making it safer, more convenient, and efficient. VANET provide smart traffic control, event allocation, and real-time information. Existing vehicles in VANET are now equipped with intelligent navigation, entertainment, and emergency applications. However, the highly connected nature of these vehicles poses a significant safety and security risk to drivers and assets which can result in life-threatening consequences. Location privacy is critical, and robust network security techniques should be used to counter threats in VANET environments. Existing schemes like obfuscation, mix-zones, and silent periods have preserved location privacy to some extent but have poor Quality of Service (QoS) and lack both efficiency and security. To address these issues, a shadowing scheme is introduced, which is an improvement of earlier schemes used for location privacy. This approach ensures better service to the vehicle by allowing precise location-based service (LBS) requests to the LBS server and uses blockchain technology for storing vehicular certificates. The inclusion of a group leader significantly reduces the time taken for implementing the scheme, improving efficiency and scalability. The anonymity set size increases over time, offering better privacy protection especially in densely populated areas. The proposed scheme overcomes drawbacks of existing techniques which includes reduced location accuracy and low-quality service in spatial obfuscation techniques, limited applicability and high tracking rate in shadow-based approaches, and reduced utility in distance-based schemes. Moreover, single point of failure and resource-intensive group formation in group-based schemes, and dependency on additional infrastructure in mix-zone-based schemes are also overcome. The proposed scheme's experimental results validate it, showing that it outperforms current state-of-the-art schemes based on metrics, such as anonymity set size, entropy, and tracking success ratio.