Privacy Preservation of Electronic Health Records in the Modern Era: A Systematic Survey

IF 23.8 1区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS
Raza Nowrozy, Khandakar Ahmed, A.S.M. Kayes, Hua Wang, Timothy R. McIntosh
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Abstract

Building a secure and privacy-preserving health data sharing framework is a topic of great interest in the healthcare sector, but its success is subject to ensuring the privacy of user data. We clarified the definitions of privacy, confidentiality and security (PCS) because these three terms have been used interchangeably in the literature. We found that researchers and developers must address the differences of these three terms when developing electronic health record (EHR) solutions. We surveyed 130 studies on EHRs, privacy-preserving techniques, and tools that were published between 2012 and 2022, aiming to preserve the privacy of EHRs. The observations and findings were summarized with the help of the identified studies framed along the survey questions addressed in the literature review. Our findings suggested that the usage of access control, blockchain, cloud-based, and cryptography techniques is common for EHR data sharing. We summarized the commonly used strategies for preserving privacy that are implemented by various EHR tools. Additionally, we collated a comprehensive list of differences and similarities between PCS. Finally, we summarized the findings in a tabular form for all EHR tools and techniques and proposed a fusion of techniques to better preserve the PCS of EHRs.

现代电子健康记录的隐私保护:系统调查
建立一个安全且能保护隐私的健康数据共享框架是医疗保健领域非常关注的话题,但其成功与否取决于能否确保用户数据的隐私。我们澄清了隐私、保密和安全 (PCS) 的定义,因为这三个术语在文献中交替使用。我们发现,研究人员和开发人员在开发电子健康记录 (EHR) 解决方案时,必须解决这三个术语之间的差异。我们调查了 2012 年至 2022 年间发表的 130 篇关于电子健康记录、隐私保护技术和工具的研究,旨在保护电子健康记录的隐私。我们根据文献综述中提出的调查问题,对已确定的研究进行了观察和发现总结。我们的研究结果表明,在电子病历数据共享中,访问控制、区块链、云技术和加密技术的使用非常普遍。我们总结了各种电子病历工具常用的隐私保护策略。此外,我们还整理了 PCS 之间的全面异同列表。最后,我们以表格形式总结了所有电子病历工具和技术的研究结果,并提出了一种技术融合方案,以更好地保护电子病历的 PCS。
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来源期刊
ACM Computing Surveys
ACM Computing Surveys 工程技术-计算机:理论方法
CiteScore
33.20
自引率
0.60%
发文量
372
审稿时长
12 months
期刊介绍: ACM Computing Surveys is an academic journal that focuses on publishing surveys and tutorials on various areas of computing research and practice. The journal aims to provide comprehensive and easily understandable articles that guide readers through the literature and help them understand topics outside their specialties. In terms of impact, CSUR has a high reputation with a 2022 Impact Factor of 16.6. It is ranked 3rd out of 111 journals in the field of Computer Science Theory & Methods. ACM Computing Surveys is indexed and abstracted in various services, including AI2 Semantic Scholar, Baidu, Clarivate/ISI: JCR, CNKI, DeepDyve, DTU, EBSCO: EDS/HOST, and IET Inspec, among others.
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