A Privacy-Preserving Healthcare Framework Using Hyperledger Fabric

Stamatellis, Charalampos; Papadopoulos, Pavlos; Pitropakis, Nikolaos; Katsikas, Sokratis K.; Buchanan, William J.

doi:10.3390/s20226587

Cited by 109 publications

(58 citation statements)

References 33 publications

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“…There are works that presented the use of another emerging technology such as blockchain, which could be combined with ML. In the work of [104,105] the authors' presented infrastructures that could protect certain private data from the stored records and display of other non-private. However, the feasibility of performing ML in data stored in their blockchain has not been tested and remains an open question.…”

Section: Related Workmentioning

confidence: 99%

Privacy and Trust Redefined in Federated Machine Learning

Papadopoulos

Abramson

Hall

et al. 2021

MAKE

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A common privacy issue in traditional machine learning is that data needs to be disclosed for the training procedures. In situations with highly sensitive data such as healthcare records, accessing this information is challenging and often prohibited. Luckily, privacy-preserving technologies have been developed to overcome this hurdle by distributing the computation of the training and ensuring the data privacy to their owners. The distribution of the computation to multiple participating entities introduces new privacy complications and risks. In this paper, we present a privacy-preserving decentralised workflow that facilitates trusted federated learning among participants. Our proof-of-concept defines a trust framework instantiated using decentralised identity technologies being developed under Hyperledger projects Aries/Indy/Ursa. Only entities in possession of Verifiable Credentials issued from the appropriate authorities are able to establish secure, authenticated communication channels authorised to participate in a federated learning workflow related to mental health data.

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Section: Related Workmentioning

confidence: 99%

Privacy and Trust Redefined in Federated Machine Learning

Papadopoulos

Abramson

Hall

et al. 2021

MAKE

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“…Therefore, a great number of studies have been conducted to resolve these issues for various blockchain applications. For example, Stamatellis et al [ 31 ] proposed a solution for privacy-preserving healthcare framework on a blockchain, which provides anonymity and unlinkability. Prada-Delgado et al [ 32 ] and Asif et al [ 33 ] provided hybrid solutions that use blockchains combined with physical unclonable functions (PUFs) for IoT and IoE, respectively, where methods for clone-proof device identification and authentication using blockchains were proposed.…”

Section: Background and Related Workmentioning

confidence: 99%

Efficient and Privacy-Preserving Energy Trading on Blockchain Using Dual Binary Encoding for Inner Product Encryption

Gaybullaev

Kwon

Kim

et al. 2021

Sensors

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The rapidly increasing expansion of distributed energy resources (DER), such as renewable energy systems and energy storage systems into the electric power system and the integration of advanced information and communication technologies enable DER owners to participate in the electricity market for grid services. For more efficient and reliable power system operation, the concept of peer-to-peer (P2P) energy trading has recently been proposed. The adoption of blockchain technology in P2P energy trading has been considered to be the most promising solution enabling secure smart contracts between prosumers and users. However, privacy concerns arise because the sensitive data and transaction records of the participants, i.e., the prosumers and the distribution system operator (DSO), become available to the blockchain nodes. Many efforts have been made to resolve this issue. A recent breakthrough in a P2P energy trading system on an Ethereum blockchain is that all bid values are encrypted using functional encryption and peer matching for trading is performed securely on these encrypted bids. Their protocol is based on a method that encodes integers to vectors and an algorithm that securely compares the ciphertexts of these vectors. However, the comparison method is not very efficient in terms of the range of possible bid values because the amount of computation grows linearly according to the size of this range. This paper addresses this challenge by proposing a new bid encoding algorithm called dual binary encoding, which dramatically reduces the amount of computation as it is only proportional to the square of the logarithm of the size of the encoding range. Moreover, we propose a practical mechanism for rebidding the remaining amount caused when the amounts from the two matching peers are not equal. Finally, the feasibility of the proposed method is evaluated by using a virtual energy trade testbed and a private Ethereum blockchain platform.

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“…The quantum key is completed by the trader and the block creator. As shown in Figure 4(m), Charalampos Stamatellis [44] uses Hyperledger to construct a hybrid identity verifier. The system has three verification entities, including health centers, hospitals, and public health.…”

Section: ) Ehr and Emr Systems Based On Hyperledgermentioning

confidence: 99%

Application of Hyperledger in the Hospital Information Systems: A Survey

2021

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Hyperledger is considered to be the most mature consortium chain technology. Compared with other blockchain technologies, Hyperledger focuses on the application development of enterprise-level standards. Due to its unique permission management, fine-grained access control, pluggable consensus algorithm and higher transaction performance, Hyperledger has been increasingly used in the hospital information systems. Although there are more and more applications of Hyperledger in the field of health care, there is currently no research to comprehensively classify and compare them. This article obtained the latest 222 articles related to Hyperledger from the of Web of science, Wordlib, and EBSCO databases. And this article used manual method to filter out the literates needed for this review. This article had comprehensively analyzed and compared the above literature, then summarized four research directions related to hospital information systems, including medical drug traceability, medical records, medical images and other medical fields (financial benefits, medical insurance, medical system performance and dynamic processing). Through in-depth analysis of all solutions, this article reconstructed the architecture diagrams of each solution that focus on Hyperledger, and showed the most intuitive comparison results. In addition, we summarized each solution, and analyzed their implementation form four aspects, including traceability, monitoring, security and privacy. Finally, the future opportunities and prospects were discussed. Four potential medical research directions of Hyperledger in DNA research, pathological image sharing, protein folding calculation and machine learning had been identified. Finally, this article discussed the opportunities and prospects for the future of hospital information systems, and summarized the applications of Hyperledger in four potential directions, including DNA research, pathological image sharing, protein folding calculation and machine learning.

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A Privacy-Preserving Healthcare Framework Using Hyperledger Fabric

Cited by 109 publications

References 33 publications

Privacy and Trust Redefined in Federated Machine Learning

Privacy and Trust Redefined in Federated Machine Learning

Efficient and Privacy-Preserving Energy Trading on Blockchain Using Dual Binary Encoding for Inner Product Encryption

Application of Hyperledger in the Hospital Information Systems: A Survey

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