Empowering Privacy: Harnessing Hyperledger Fabric to Safeguard EHR Systems

Vidhi Thakkar, Vrushank Manharlal Shah
{"title":"Empowering Privacy: Harnessing Hyperledger Fabric to Safeguard EHR Systems","authors":"Vidhi Thakkar, Vrushank Manharlal Shah","doi":"10.3844/jcssp.2023.1292.1304","DOIUrl":null,"url":null,"abstract":"The Blockchain boom began with the debut of Bitcoin. The application of blockchain technology is expanding rapidly. Various sectors such as supply chain, logistics, research, healthcare, government, banking, media, and entertainment have already embraced this ground-breaking, decentralized technology. The healthcare industry is at the top of the list with significant blockchain potential. This article discusses the permissioned blockchain powered by Hyperledger Fabric and its privacy-preserving features like identity mixer, multichannel, private data collections, and transient field. This study considers the EHR systems scenario and proves how these privacy protection techniques of Fabric could protect the privacy of healthcare organizations' sensitive data. We evaluate existing studies on the use of the Hyperledger Fabric framework for EHR systems. We discovered that their implementation has data privacy and user privacy concerns that can be addressed in our future studies.","PeriodicalId":40005,"journal":{"name":"Journal of Computer Science","volume":"42 3","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Computer Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3844/jcssp.2023.1292.1304","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

The Blockchain boom began with the debut of Bitcoin. The application of blockchain technology is expanding rapidly. Various sectors such as supply chain, logistics, research, healthcare, government, banking, media, and entertainment have already embraced this ground-breaking, decentralized technology. The healthcare industry is at the top of the list with significant blockchain potential. This article discusses the permissioned blockchain powered by Hyperledger Fabric and its privacy-preserving features like identity mixer, multichannel, private data collections, and transient field. This study considers the EHR systems scenario and proves how these privacy protection techniques of Fabric could protect the privacy of healthcare organizations' sensitive data. We evaluate existing studies on the use of the Hyperledger Fabric framework for EHR systems. We discovered that their implementation has data privacy and user privacy concerns that can be addressed in our future studies.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
授权隐私:利用超级账本结构保护电子病历系统
区块链的繁荣始于比特币的首次亮相。区块链技术的应用正在迅速扩大。供应链、物流、研究、医疗保健、政府、银行、媒体和娱乐等各个部门已经采用了这种开创性的分散式技术。医疗保健行业是区块链潜力巨大的行业。本文讨论了由Hyperledger Fabric提供支持的许可区块链及其隐私保护功能,如身份混合器、多通道、私有数据收集和瞬态字段。本研究考虑了EHR系统场景,并证明了Fabric的这些隐私保护技术如何保护医疗保健组织敏感数据的隐私。我们评估了现有的关于在EHR系统中使用超级账本结构框架的研究。我们发现它们的实现有数据隐私和用户隐私问题,可以在我们未来的研究中解决。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Journal of Computer Science
Journal of Computer Science Computer Science-Computer Networks and Communications
CiteScore
1.70
自引率
0.00%
发文量
92
期刊介绍: Journal of Computer Science is aimed to publish research articles on theoretical foundations of information and computation, and of practical techniques for their implementation and application in computer systems. JCS updated twelve times a year and is a peer reviewed journal covers the latest and most compelling research of the time.
期刊最新文献
Features of the Security System Development of a Computer Telecommunication Network Performance Assessment of CPU Scheduling Algorithms: A Scenario-Based Approach with FCFS, RR, and SJF Website-Based Educational Application to Help MSMEs in Indonesia Develop A Multi-Split Cross-Strategy for Enhancing Machine Learning Algorithms Prediction Results with Data Generated by Conditional Generative Adversarial Network Improving the Detection of Mask-Wearing Mistakes by Deep Learning
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1