1. Blockchain-Based Privacy-Protected Reputation Model for Internet of Vehicles

The Internet of Vehicles (IoV) is a promising emerging technology that could significantly contribute to the development of high-level transportation infrastructure. However, there are constraints to the traditional reputation management approach in the IoV sector. These include a small detection radius, the possibility of privacy issues, and the use of unstable centralised reputation servers. This chapter provides a reputation model for the IoV that makes use of blockchain technology to guarantee anonymity, so addressing these concerns. Traditional IoV reputation systems have a major flaw due to their reliance on a single reputation server. The integrity and dependability of reputation assessments are at risk due to the presence of potential vulnerabilities in these systems, which could allow for unauthorised data alteration or theft. Additionally, one could argue that the current IoV reputation method does not offer sufficient protection for user anonymity. The evaluation of car reputations frequently requires the inclusion of personal information, such as driving habits and location data. Lastly, it is important to note that there exists a distance threshold beyond which the aforementioned reputation technique may encounter difficulties in accurately detecting and identifying vehicles that exhibit malevolent behaviour. The aforementioned proposal employs a distributed reputation update method based on blockchain technology in order to address these issues. This strategy enhances the precision of reputation analysis by obviating the necessity for a centralised reputation server. Furthermore, the utilisation of a multi-key, fully homomorphic encryption methodology is employed for both the encryption and computation of evaluation data, thereby substantially mitigating the potential for privacy breaches. In order to mitigate the possibility of rogue vehicles eluding detection through reputation upgrades, a novel adaptive retroactive time interval adjustment approach has been devised. The simulation results indicate that the proposed approach effectively preserves user privacy while maintaining high detection rates for malicious vehicles, and also minimises the occurrence of false positives. Furthermore, the model exhibits a detection rate that surpasses gold-standard techniques by 32%.