基于区块链的以患者为中心的传染病相关检测记录细粒度访问控制机制
PatCen: A blockchain-based patient-centric mechanism for the granular access control of infectious disease-related test records.
机构信息
Department of Mathematics and Computer Science, Faculty of Science, Chulalongkorn University, Bangkok, Thailand.
Department of Computer Science, COMSATS University Islamabad, Islamabad, Pakistan.
出版信息
PLoS One. 2024 Sep 18;19(9):e0310407. doi: 10.1371/journal.pone.0310407. eCollection 2024.
The recent global outbreaks of infectious diseases such as COVID-19, yellow fever, and Ebola have highlighted the critical need for robust health data management systems that can rapidly adapt to and mitigate public health emergencies. In contrast to traditional systems, this study introduces an innovative blockchain-based Electronic Health Record (EHR) access control mechanism that effectively safeguards patient data integrity and privacy. The proposed approach uniquely integrates granular data access control mechanism within a blockchain framework, ensuring that patient data is only accessible to explicitly authorized users and thereby enhancing patient consent and privacy. This system addresses key challenges in healthcare data management, including preventing unauthorized access and overcoming the inefficiencies inherent in traditional access mechanisms. Since the latency is a sensitive factor in healthcare data management, the simulations of the proposed model reveal substantial improvements over existing benchmarks in terms of reduced computing overhead, increased throughput, minimized latency, and strengthened overall security. By demonstrating these advantages, the study contributes significantly to the evolution of health data management, offering a scalable, secure solution that prioritizes patient autonomy and privacy in an increasingly digital healthcare landscape.
最近全球爆发的传染病疫情,如 COVID-19、黄热病和埃博拉病毒,凸显了建立强大的卫生数据管理系统的重要性,这些系统可以快速适应和减轻突发公共卫生事件。与传统系统相比,本研究提出了一种创新的基于区块链的电子健康记录(EHR)访问控制机制,能够有效地保护患者数据的完整性和隐私。该方法独特地将细粒度的数据访问控制机制集成到区块链框架中,确保患者数据仅对明确授权的用户可用,从而增强了患者的同意和隐私。该系统解决了医疗保健数据管理中的关键挑战,包括防止未经授权的访问和克服传统访问机制固有的效率低下问题。由于延迟是医疗保健数据管理中的一个敏感因素,因此与现有基准相比,所提出模型的模拟在减少计算开销、提高吞吐量、最小化延迟和增强整体安全性方面有了显著的改进。通过展示这些优势,本研究为医疗保健数据管理的发展做出了重大贡献,提供了一个可扩展、安全的解决方案,在日益数字化的医疗保健环境中优先考虑患者的自主权和隐私。
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