Design of a dynamic trust management and defense decision system for shared vehicle data based on blockchain and deep reinforcement learning - Jinxiang Chen; Yan Li; Jiaxing Deng; Beicheng Qin; Chengcai He; Qiangsheng Huang; Jingchun Wu (Scientific Reports)
Abstract 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.