Blockchain technology has been changing the nature of several businesses, from supply chain management to electronic record management systems and copyright management to healthcare applications. It provides a resilient and secure platform for modifications due to its distributed and shared nature and cryptographic functions. Each new technology, however, comes with its challenges alongside its opportunities. Previously, we performed a systematic literature review (SLR) to explore how blockchain technology potentially benefits health domain applications. The previous SLR included 27 formal literature papers from 2016 to 2020. Noticing that blockchain technology is rapidly growing, we extended the previous SLR with a multivocal literature review (MLR) approach to present the state of the art in this study. We focused on understanding to what degree blockchain could answer the challenges inherited in the health domain and whether blockchain technology may bring new challenges to health applications. The MLR consists of 78 sources of formal literature and 23 sources of gray literature from 2016 to 2021. As a result of this study, we specified 17 health domain challenges that can be categorized into four groups: (i) meeting regulatory requirements and public health surveillance, (ii) ensuring security and privacy, (iii) ensuring interoperability, and (iv) preventing waste of resources. The analysis shows that blockchain makes significant contributions to the solutions of these challenges. However, 10 new pitfalls come with adopting the technology in the health domain: the inability to delete sensitive data once it is added to a chain, limited ability to keep large-scale data in a blockchain, and performance issues. The data we extracted during the MLR is available in a publicly accessible online repository.
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http://dx.doi.org/10.1007/s11227-022-04772-1 | DOI Listing |
Sci Rep
January 2025
Xinjiang Vocational and Technical College of Communications, Urumqi, Xinjiang, 831401, China.
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View Article and Find Full Text PDFBrain Inform
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Department of Computing, Glasgow Caledonian University, Glasgow, G4 0BA, Scotland.
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November 2024
Management Science and Information Systems Department, Rutgers University, Newark, NJ 07102-3122 USA.
Interest in supporting Federated Learning (FL) using blockchains has grown significantly in recent years. However, restricting access to the trained models only to actively participating nodes remains a challenge even today. To address this concern, we propose a methodology that incentivizes model parameter sharing in an FL setup under Local Differential Privacy (LDP).
View Article and Find Full Text PDFSci Rep
January 2025
Torrens University Australia, Fortitude Valley, QLD 4006, Leaders Institute, 76 Park Road, Woolloongabba, QLD 4102, Brisbane, Queensland, Australia.
Sensors (Basel)
January 2025
Department of Computer Science and Engineering, Yanbu Industrial College, Royal Commission for Jubail and Yanbu, Yanbu Industrial City 41912, Saudi Arabia.
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