Blockchain‐Enabled Privacy‐Preserving Anomaly Detection and Reputation Framework for <scp>VANETs</scp>

ABSTRACT To address issues in traditional vehicular network trust mechanisms such as the untrustworthiness of centralized reputation servers, threats to user privacy, and limited detection scope a blockchain‐based vehicular network anomaly detection and reputation model with privacy protection is proposed. Leveraging blockchain technology, a distributed and trustworthy reputation update framework for vehicular networks is designed. The evaluation data is encrypted and computed using a multi‐key fully homomorphic encryption technique, which reduces the danger of user privacy leakage. An adaptive adjustment approach is implemented for the retrospective time interval to improve anomaly detection. This strategy stops hostile cars from evading detection by exploiting reputation updates. According to the simulation results, which demonstrate accuracy with low false positive rates in identifying malicious cars, the suggested method effectively protects user privacy while attaining high anomaly detection rates. The detection rate for unusual vehicle behavior is increased by 38.56% as compared to conventional systems. This increased detection rate implies that the technique is better at differentiating between typical and anomalous processes, which improves network dependability and safety.