A Blockchain-Based Fine-Grained Reputation-Enhanced Consensus Mechanism for Secure Health Data Trading

With the deep integration of wearable devices and information technology, health data collection has become more efficient and accurate. This has brought about significant changes to health management and public health. However, due to the high sensitivity and privacy of health data, data sharing faces major challenges. Most existing studies focus on encryption algorithms and access control to ensure data security. They often ignore the credibility of data providers, which affects data quality and reduces user participation. To address these issues, this article proposes a blockchain-based and reputation-enhanced health data trading model. A fine-grained reputation value calculation method based on the Beta distribution is introduced to objectively evaluate the behavior of data providers. Based on this, a consensus mechanism linked to reputation value is designed to improve consensus efficiency and avoid centralization of node selection. Furthermore, this article uses evolutionary game theory to analyze the reward and punishment mechanism in the trading process. It explores the dynamic balance between platform cost and user willingness to share. Experimental results show that the model ensures secure health data sharing, while effectively improving data usability, system fairness, and user participation.