Research Output

Privacy and Trust Redefined in Federated Machine Learning

  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.

  • Type:

    Article

  • Date:

    29 March 2021

  • Publication Status:

    Published

  • DOI:

    10.3390/make3020017

  • Cross Ref:

    10.3390/make3020017

  • ISSN:

    2504-4990

  • Funders:

    European Commission; Edinburgh Napier Funded

Citation

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

Authors

Keywords

trust; machine learning; federated learning; decentralised identifiers; verifiable credentials

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