A secure and privacy-preserving authentication framework for Connected and Autonomous Vehicles based on DRG-PBFT and zero-knowledge proof

Abstract

Connected and Autonomous Vehicles (CAVs) are pivotal to advancing Intelligent Transportation Systems (ITS) but introduce significant security and privacy challenges, particularly in dynamic environments requiring real-time data exchange. The existing security measures and consensus mechanisms, such as Practical Byzantine Fault Tolerance (PBFT), are susceptible to various attacks, including identity forgery, unauthorized access, and compromised safety testing, and suffer from scalability and latency issues. This study addresses these challenges by proposing a Dynamic Reputation Grouping-based PBFT (DRG-PBFT) approach, integrated with Simulation Extractable Zero-Knowledge Succinct Non-Interactive Arguments of Knowledge (SE-ZK-SNARKS). The proposed framework leverages reputation-based dynamic grouping to enhance consensus efficiency and reduce communication overhead. SE-ZK-SNARKS provide anonymity and privacy-preserving identity authentication, enabling CAVs to prove their legitimacy without revealing sensitive information. The proposed framework has been validated through extensive simulations using the NS-3 network simulator integrated with blockchain. The simulation results demonstrate that our proposed approach outperforms existing methods, achieving reduced consensus latency, communication overhead, authentication time and improved throughput. Overall, the findings and methodologies presented in this study address critical challenges in securing CAV communications while maintaining scalability and efficiency and can serve as a valuable reference for researchers and practitioners aiming to improve the safety and reliability of CAVs in real-time environments.