Tong Min Kim , Taehoon Ko , Byoung Woo Hwang , Hyung Goo Paek , Wan Yeon Lee
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引用次数: 0
Abstract
Conventional personal health record (PHR) management systems are centralized, making them vulnerable to privacy breaches and single points of failure. Despite progress in standardizing healthcare data with the FHIR format, hospitals often lack efficient platforms for transferring PHRs, leading to redundant tests and delayed treatments. To address these challenges, we propose a decentralized PHR management system leveraging Personal Data Stores (PDS) and Decentralized Identifiers (DIDs) in line with the Web 3.0 model. Our system features secure interoperability and personal identification masking. Interoperability is achieved through DID digital certificates for verifying PDS addresses and a dynamic access key (AK) system to minimize credential exposure. Data de-identification, including anonymization and encryption, ensures privacy and prevents unauthorized access. We developed a prototype using the Solid open-source library and Hyperledger Aries protocol. Testing showed efficient performance, with DID validations and AK generation under one second, and data operations for 500 MB-sized PHRs completing in two seconds. De-identification processes were both effective and timely. The system demonstrated the ability to manage PHRs securely, empower users with control over their healthcare data, facilitate seamless and secure data transfer between patients and medical entities, and prevent exposure of sensitive information. This approach advances decentralized PHR management, supporting improved healthcare outcomes and patient experiences in the digital era.
期刊介绍:
Computational and Structural Biotechnology Journal (CSBJ) is an online gold open access journal publishing research articles and reviews after full peer review. All articles are published, without barriers to access, immediately upon acceptance. The journal places a strong emphasis on functional and mechanistic understanding of how molecular components in a biological process work together through the application of computational methods. Structural data may provide such insights, but they are not a pre-requisite for publication in the journal. Specific areas of interest include, but are not limited to:
Structure and function of proteins, nucleic acids and other macromolecules
Structure and function of multi-component complexes
Protein folding, processing and degradation
Enzymology
Computational and structural studies of plant systems
Microbial Informatics
Genomics
Proteomics
Metabolomics
Algorithms and Hypothesis in Bioinformatics
Mathematical and Theoretical Biology
Computational Chemistry and Drug Discovery
Microscopy and Molecular Imaging
Nanotechnology
Systems and Synthetic Biology