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Privacy and Trust Redefined in Federated Machine Learning

1
Blockpass ID Lab, School of Computing, Edinburgh Napier University, Edinburgh EH10 5DT, UK
2
Eight Bells LTD, Nicosia 2002, Cyprus
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Author to whom correspondence should be addressed.
Academic Editor: Edgar Weippl
Mach. Learn. Knowl. Extr. 2021, 3(2), 333-356; https://doi.org/10.3390/make3020017
Received: 30 January 2021 / Revised: 5 March 2021 / Accepted: 24 March 2021 / Published: 29 March 2021
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. View Full-Text
Keywords: trust; machine learning; federated learning; decentralised identifiers; verifiable credentials trust; machine learning; federated learning; decentralised identifiers; verifiable credentials
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MDPI and ACS Style

Papadopoulos, P.; Abramson, W.; Hall, A.J.; Pitropakis, N.; Buchanan, W.J. Privacy and Trust Redefined in Federated Machine Learning. Mach. Learn. Knowl. Extr. 2021, 3, 333-356. https://doi.org/10.3390/make3020017

AMA Style

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

Chicago/Turabian Style

Papadopoulos, Pavlos, Will Abramson, Adam J. Hall, Nikolaos Pitropakis, and William J. Buchanan 2021. "Privacy and Trust Redefined in Federated Machine Learning" Machine Learning and Knowledge Extraction 3, no. 2: 333-356. https://doi.org/10.3390/make3020017

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