Chen Jinxiang, Li Yan, Deng Jiaxing, Qin Beicheng, He Chengcai, Huang Qiangsheng, Wu Jingchun
Shenzhen Ruipengfei Mold Co., Ltd, Shenzhen, 518000, China.
Sci Rep. 2025 Jul 22;15(1):26662. doi: 10.1038/s41598-025-11511-y.
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.
共享车辆数据系统中的信任管理面临重大挑战，需要创新方法。为解决这些问题，引入了一种将基于区块链的分布式信任管理与深度强化学习（DRL）相结合的数据分析系统。所提出的系统包括两个核心组件：（1）用户信任评估模型：采用贝叶斯统计方法估计用户可信度，将历史交互记录用作先验信息。区块链技术分离了数据链和信任链，实现了分布式架构，增强了数据存储安全性和信任管理的稳健性。（2）行为建模与防御策略：将共享车辆服务过程和用户行为概念化为马尔可夫决策过程。使用深度Q网络（DQN）算法，系统通过多维数据交互识别最优防御策略。使用自动驾驶数据集（https://github.com/DRL-CASIA/Autonomous-Driving-Dataset-Open）进行性能评估，采用以下关键指标：（1）信任评估准确性：评估系统评估用户信任的精度。与集中式方法相比，基于区块链的方法将准确性提高了约16%，证明了其可靠性。（2）平均系统奖励：表示实施防御策略的预期回报。与Q学习相比，基于DQN的系统性能提升超过20%，突出了其决策效率。（3）恶意行为检测率：衡量系统检测和处理恶意活动的能力。所提出的模型实现了约93%的检测率，提高了超过15%，反映了其先进的防御能力。（4）服务响应时间：评估系统响应用户请求的效率。响应时间减少超过11%强调了操作速度的提高。实验结果验证了所提出系统在解决信任管理和决策挑战方面的有效性。通过将区块链的分散存储能力与DRL的动态优化潜力相结合，该系统展示了一种在复杂场景中进行分布式数据分析的可扩展且高效的方法。
