Machine learning models in trusted research environments - understanding operational risks.

Journal: International journal of population data science
PMID: 38414545

Abstract

INTRODUCTION: Trusted research environments (TREs) provide secure access to very sensitive data for research. All TREs operate manual checks on outputs to ensure there is no residual disclosure risk. Machine learning (ML) models require very large amount of data; if this data is personal, the TRE is a well-established data management solution. However, ML models present novel disclosure risks, in both type and scale.

Authors

  • Felix Ritchie
    Bristol Business School, University of the West of England, Coldharbour Lane, Bristol BS16 1QY.
  • Amy Tilbrook
    University of Edinburgh, South Bridge, Edinburgh EH8 9YL.
  • Christian Cole
    Division of Population Health and Genomics, Ninewells Hospital and Medical School, Dundee DD1 9SY.
  • Emily Jefferson
    Division of Population Health and Genomics, Ninewells Hospital and Medical School, Dundee DD1 9SY.
  • Susan Krueger
    Health Informatics Centre, Ninewells Hospital and Medical School, Dundee DD1 9SY.
  • Esma Mansouri-Benssassi
    AffectiveHalo Ltd, Tom Morris Drive, St Andrews.KY16 8HS.
  • Simon Rogers
    NHS National Services Scotland, Gyle Square, 1 South Gyle Crescent, Edinburgh EH12 9EB.
  • Jim Smith
    School of Computer Science and Creative Technologies, University of the West of England, Coldharbour Lane, Bristol BS16 1QY.

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