Design of a dynamic trust management and defense decision system for shared vehicle data based on blockchain and deep reinforcement learning.

Trust management in shared vehicle data systems presents significant challenges, necessitating innovative approaches. A data analysis system integrating blockchain-based distributed trust management with deep reinforcement learning (DRL) is introduced to address these issues. The proposed system includes two core components: (1) User Trust Evaluation Model: Bayesian statistical methods are employed to estimate user credibility, utilizing historical interaction records as prior information. Blockchain technology separates the data chain and trust chain, enabling a distributed architecture that enhances data storage security and trust management robustness. (2) Behavioral Modeling and Defensive Strategies: The shared vehicle service process and user behavior are conceptualized as a Markov Decision Process. Using the Deep Q-Network (DQN) algorithm, the system identifies optimal defensive strategies through multidimensional data interactions. Performance evaluation is conducted using the Autonomous Driving Dataset ( https://github.com/DRL-CASIA/Autonomous-Driving-Dataset-Open ), with the following key metrics: (1) Trust Evaluation Accuracy: Assesses the precision of the system in evaluating user trust. The blockchain-based approach enhances accuracy by approximately 16% compared to centralized methods, demonstrating its reliability. (2) Average System Reward: Indicates the expected return from implementing defensive strategies. The DQN-based system achieves a performance increase exceeding 20% compared to Q-learning, highlighting its decision-making efficacy. (3) Malicious Behavior Detection Rate: Measures the system's ability to detect and address malicious activities. The proposed model attains a detection rate of approximately 93%, an improvement of over 15%, reflecting its advanced defensive capabilities. (4) Service Response Time: Evaluates the system's efficiency in responding to user requests. A reduction of more than 11% in response time underscores the enhanced operational speed. Experimental results validate the effectiveness of the proposed system in addressing trust management and decision-making challenges. By combining blockchain's decentralized storage capabilities with DRL's dynamic optimization potential, the system demonstrates a scalable and efficient approach for distributed data analysis in complex scenarios.