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

Area of Science:

• Intelligent Transportation Systems (ITS)
• Cybersecurity
• Blockchain Technology

Background:

• Connected and Autonomous Vehicles (CAVs) face significant security and privacy risks in real-time data exchange.
• Existing consensus mechanisms like Practical Byzantine Fault Tolerance (PBFT) struggle with scalability, latency, and attacks such as identity forgery.

Purpose of the Study:

• To propose a novel security framework, Dynamic Reputation Grouping-based PBFT (DRG-PBFT) integrated with Simulation Extractable Zero-Knowledge Succinct Non-Interactive Arguments of Knowledge (SE-ZK-SNARKS).
• To enhance consensus efficiency, reduce communication overhead, and ensure privacy-preserving identity authentication for CAVs.

Main Methods:

• Developed a DRG-PBFT approach incorporating reputation-based dynamic grouping for improved consensus.
• Integrated SE-ZK-SNARKS for anonymous and privacy-preserving identity verification of CAVs.
• Validated the framework using NS-3 network simulations combined with blockchain.

Main Results:

• The proposed DRG-PBFT with SE-ZK-SNARKS demonstrated superior performance compared to existing methods.
• Achieved reduced consensus latency, lower communication overhead, and decreased authentication time.
• Showcased improved system throughput and enhanced security for CAV communications.

Conclusions:

• The DRG-PBFT and SE-ZK-SNARKS framework effectively addresses critical security and privacy challenges in CAVs.
• The proposed solution offers a scalable and efficient method for securing real-time data exchange in intelligent transportation systems.
• This study provides a valuable reference for advancing the safety and reliability of CAVs.