A Blockchain-based Data Governance with Privacy and Provenance: a case study for e-Prescription
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Description
Real-world applications in healthcare and supply chain domains produce, exchange, and share data in a multi-stakeholder environment. Data owners want to control their data and privacy in such settings. On the other hand, data consumers demand methods to understand when, how, and who produced the data. These requirements necessitate data governance frameworks that guarantee data provenance, privacy protection, and consent management. We introduce a decentralized data governance framework based on blockchain technology and proxy re-encryption to let data owners control and track their data through privacy-enhancing and consent management mechanisms. Besides, our framework allows the data consumers to understand data lineage through a blockchain-based provenance mechanism. We have used Digital e-prescription as the use case since it has multiple stakeholders and sensitive data while enabling the medical fraternity to manage patients' prescription data, involving patients as data owners, doctors, and pharmacists as data consumers. Our proof-of-concept implementation and evaluation results based on CosmWasm and pyUmbral PRE show that the proposed decentralized system guarantees transparency, privacy, and trust with minimal overhead.
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ID Code: | 228430 | ||||
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Item Type: | Chapter in Book, Report or Conference volume (Conference contribution) | ||||
Series Name: | IEEE International Conference on Blockchain and Cryptocurrency, ICBC 2022 | ||||
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Measurements or Duration: | 5 pages | ||||
Additional URLs: | |||||
DOI: | 10.1109/ICBC54727.2022.9805545 | ||||
ISBN: | 978-1-6654-9539-4 | ||||
Pure ID: | 105988336 | ||||
Divisions: | Current > Research Centres > Centre for Clean Energy Technologies & Practices Current > QUT Faculties and Divisions > Faculty of Science Current > Schools > School of Computer Science |
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Copyright Owner: | 2022 IEEE | ||||
Copyright Statement: | Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | ||||
Deposited On: | 23 Mar 2022 00:59 | ||||
Last Modified: | 30 Jul 2024 00:40 |
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