  Artificial intelligence (AI) applications in healthcare and medicine have
increased in recent years. To enable access to personal data, Trusted Research
environments (TREs) provide safe and secure environments in which researchers
can access sensitive personal data and develop Artificial Intelligence (AI) and
Machine Learning models. However currently few TREs support the use of
automated AI-based modelling using Machine Learning. Early attempts have been
made in the literature to present and introduce privacy preserving machine
learning from the design point of view [1]. However, there exists a gap in the
practical decision-making guidance for TREs in handling models disclosure.
Specifically, the use of machine learning creates a need to disclose new types
of outputs from TREs, such as trained machine learning models. Although TREs
have clear policies for the disclosure of statistical outputs, the extent to
which trained models can leak personal training data once released is not well
understood and guidelines do not exist within TREs for the safe disclosure of
these models.
  In this paper we introduce the challenge of disclosing trained machine
learning models from TREs. We first give an overview of machine learning models
in general and describe some of their applications in healthcare and medicine.
We define the main vulnerabilities of trained machine learning models in
general. We also describe the main factors affecting the vulnerabilities of
disclosing machine learning models. This paper also provides insights and
analyses methods that could be introduced within TREs to mitigate the risk of
privacy breaches when disclosing trained models.
