Integrated Edge Computing and Blockchain: A General Medical Data Sharing Framework

IF 5.1 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS IEEE Transactions on Emerging Topics in Computing Pub Date : 2023-12-25 DOI:10.1109/TETC.2023.3344655
Zongjin Li;Jie Zhang;Jian Zhang;Ya Zheng;Xunjie Zong
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Abstract

Medical data sharing is crucial to enhance diagnostic efficiency and improve the quality of medical data analysis. However, related endeavors face obstacles due to insufficient collaboration among medical institutions, and traditional cloud-based sharing platforms lead to concerns regarding security and privacy. To overcome these challenges, the paper introduces MSNET, a novel framework that seamlessly combines blockchain and edge computing. Data traceability and access control are ensured by employing blockchain as a security layer. The blockchain stores only data summaries instead of complete medical data, thus enhancing scalability and transaction efficiency. The raw medical data are securely processed on edge servers within each institution, with data standardization and keyword extraction. To facilitate data access and sharing among institutions, smart contracts are designed to promote transparency and data accuracy. Moreover, a supervision mechanism is established to maintain a trusted environment, provide reliable evidence against dubious data-sharing practices, and encourage institutions to share data voluntarily. This novel framework effectively overcomes the limitations of traditional blockchain solutions, offering an efficient and secure method for medical data sharing and thereby fostering collaboration and innovation in the healthcare industry.
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集成边缘计算和区块链:通用医疗数据共享框架
医疗数据共享对于提高诊断效率和医疗数据分析质量至关重要。然而,由于医疗机构之间合作不足,传统的云共享平台在安全和隐私方面存在隐忧,相关工作面临重重障碍。为了克服这些挑战,本文介绍了将区块链和边缘计算无缝结合的新型框架 MSNET。通过采用区块链作为安全层,确保了数据的可追溯性和访问控制。区块链只存储数据摘要,而不是完整的医疗数据,从而提高了可扩展性和交易效率。原始医疗数据在各机构内部的边缘服务器上进行安全处理,并进行数据标准化和关键词提取。为了方便机构间的数据访问和共享,设计了智能合约,以提高透明度和数据准确性。此外,还建立了监督机制,以维护可信环境,提供可靠证据打击可疑的数据共享行为,并鼓励各机构自愿共享数据。这种新型框架有效克服了传统区块链解决方案的局限性,为医疗数据共享提供了一种高效、安全的方法,从而促进了医疗行业的合作与创新。
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来源期刊
IEEE Transactions on Emerging Topics in Computing
IEEE Transactions on Emerging Topics in Computing Computer Science-Computer Science (miscellaneous)
CiteScore
12.10
自引率
5.10%
发文量
113
期刊介绍: IEEE Transactions on Emerging Topics in Computing publishes papers on emerging aspects of computer science, computing technology, and computing applications not currently covered by other IEEE Computer Society Transactions. Some examples of emerging topics in computing include: IT for Green, Synthetic and organic computing structures and systems, Advanced analytics, Social/occupational computing, Location-based/client computer systems, Morphic computer design, Electronic game systems, & Health-care IT.
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Table of Contents Front Cover IEEE Transactions on Emerging Topics in Computing Information for Authors Special Section on Emerging Social Computing DALTON - Deep Local Learning in SNNs via local Weights and Surrogate-Derivative Transfer
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