All Outputs (5)

Trusted Threat Intelligence Sharing in Practice and Performance Benchmarking through the Hyperledger Fabric Platform (2022)
Journal Article
Ali, H., Ahmad, J., Jaroucheh, Z., Papadopoulos, P., Pitropakis, N., Lo, O., …Buchanan, W. J. (2022). Trusted Threat Intelligence Sharing in Practice and Performance Benchmarking through the Hyperledger Fabric Platform. Entropy, 24(10), Article 1379. https://doi.org/10.3390/e24101379

Historically, threat information sharing has relied on manual modelling and centralised network systems, which can be inefficient, insecure, and prone to errors. Alternatively, private blockchains are now widely used to address these issues and impro... Read More about Trusted Threat Intelligence Sharing in Practice and Performance Benchmarking through the Hyperledger Fabric Platform.

PyDentity: A playground for education and experimentation with the hyperledger verifiable information exchange platform (2021)
Journal Article
Abramson, W., Papadopoulos, P., Pitropakis, N., & Buchanan, W. J. (2021). PyDentity: A playground for education and experimentation with the hyperledger verifiable information exchange platform. Software Impacts, 9, Article 100101. https://doi.org/10.1016/j.simpa.2021.100101

PyDentity lowers the entry barrier for parties interested in experimenting with the Hyperledger’s verifiable information exchange platform. It enables educators, developers and researchers to configure and initialise a set of actors easily as associa... Read More about PyDentity: A playground for education and experimentation with the hyperledger verifiable information exchange platform.

Evaluating Trust Assurance in Indy-Based Identity Networks Using Public Ledger Data (2021)
Journal Article
Abramson, W., Hickman, N., & Spencer, N. (2021). Evaluating Trust Assurance in Indy-Based Identity Networks Using Public Ledger Data. Frontiers in Block Chain, 4, Article 622090. https://doi.org/10.3389/fbloc.2021.622090

Privacy and Trust Redefined in Federated Machine Learning (2021)
Journal Article
Papadopoulos, P., Abramson, W., Hall, A. J., Pitropakis, N., & Buchanan, W. J. (2021). Privacy and Trust Redefined in Federated Machine Learning. Machine Learning and Knowledge Extraction, 3(2), 333-356. https://doi.org/10.3390/make3020017

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... Read More about Privacy and Trust Redefined in Federated Machine Learning.

Trust-by-Design: Evaluating Issues and Perceptions within Clinical Passporting (2020)
Journal Article
Abramson, W., van Deursen, N. E., & Buchanan, W. J. (2020). Trust-by-Design: Evaluating Issues and Perceptions within Clinical Passporting. Blockchain in Healthcare Today, 3, https://doi.org/10.30953/bhty.v3.140

A substantial administrative burden is placed on healthcare professionals as they manage and progress through their careers. Identity verification, pre-employment screening and appraisals: the bureaucracy associated with each of these processes takes... Read More about Trust-by-Design: Evaluating Issues and Perceptions within Clinical Passporting.