Machine learning models disclosure from trusted research environments (TRE), challenges and opportunities

Esma Mansouri-Benssassi, Simon Rogers, Jim Smith, Felix Ritchie, Emily Jefferson

Research output: Working paper/PreprintPreprint

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Abstract

Trusted Research environments (TRE)s are safe and secure environments in which researchers can access sensitive data. With the growth and diversity of medical data such as Electronic Health Records (EHR), Medical Imaging and Genomic data, there is an increase in the use of Artificial Intelligence (AI) in general and the subfield of Machine Learning (ML) in particular in the healthcare domain. This generates the desire to disclose new types of outputs from TREs, such as trained machine learning models. Although specific guidelines and policies exists for statistical disclosure controls in TREs, they do not satisfactorily cover these new types of output request. In this paper, we define some of the challenges around the application and disclosure of machine learning for healthcare within TREs. We describe various vulnerabilities the introduction of AI brings to TREs. We also provide an introduction to the different types and levels of risks associated with the disclosure of trained ML models. We finally describe the new research opportunities in developing and adapting policies and tools for safely disclosing machine learning outputs from TREs.
Original languageEnglish
Place of PublicationCornell University
PublisherarXiv
Number of pages24
Publication statusPublished - 10 Nov 2021

Keywords

  • cs.CR
  • cs.AI
  • cs.CY

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