Abstract
Vehicular networks face increasing challenges to ensure security, privacy, and efficiency in dynamic communication environments. Current solutions often lack adaptability to evolving threats and efficient mechanisms for preserving privacy and reducing computational overhead. This study proposes PrivNet, a framework that integrates generative AI with advanced cryptographic and trust mechanisms to address these limitations. The framework comprises the Quantum-Augmented Holographic Cryptographic System (QAHCS) for dynamic and secure key generation, the Neural Overlap Privacy System (NOPS) for adaptive pseudonym morphing and entropy-driven identity obfuscation, and the Self-Supervised Generative Anomaly Detection (SS-GAI) module for real-time threat modeling and counter-anomaly injection. The system also incorporates Hyperledger Mesh for energy-efficient and secure transaction validation. Simulations were performed using NSL-KDD, CICIDS2017, and Car-Hacking / VeReMi datasets for 300 minutes. The results demonstrate a 12% improvement in detection accuracy, a 23% improvement in energy efficiency, and a 22% reduction in resource utilization.