Bristol Business School, University of the West of England, Coldharbour Lane, Bristol BS16 1QY.
University of Edinburgh, South Bridge, Edinburgh EH8 9YL.
Int J Popul Data Sci. 2023 Dec 14;8(1):2165. doi: 10.23889/ijpds.v8i1.2165. eCollection 2023.
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.
As part of a series on ML disclosure risk in TREs, this article is intended to introduce TRE managers to the conceptual problems and work being done to address them.
We demonstrate how ML models present a qualitatively different type of disclosure risk, compared to traditional statistical outputs. These arise from both the nature and the scale of ML modelling.
We show that there are a large number of unresolved issues, although there is progress in many areas. We show where areas of uncertainty remain, as well as remedial responses available to TREs.
At this stage, disclosure checking of ML models is very much a specialist activity. However, TRE managers need a basic awareness of the potential risk in ML models to enable them to make sensible decisions on using TREs for ML model development.
可信研究环境（TRE）为研究提供了对非常敏感数据的安全访问。所有 TRE 都对输出进行手动检查，以确保不存在残留的披露风险。机器学习（ML）模型需要大量的数据；如果这些数据是个人的，那么 TRE 是一种成熟的数据管理解决方案。然而，ML 模型在类型和规模上都带来了新的披露风险。
作为 TRE 中 ML 披露风险系列文章的一部分，本文旨在向 TRE 管理人员介绍概念性问题和正在努力解决这些问题的工作。
我们展示了与传统统计输出相比，ML 模型如何呈现出一种性质和规模都不同的披露风险。
我们表明，尽管在许多领域都取得了进展，但仍存在大量未解决的问题。我们展示了仍存在不确定性的领域，以及 TRE 可用的补救措施。
在现阶段，ML 模型的披露检查非常专业。然而，TRE 管理人员需要对 ML 模型中的潜在风险有基本的认识，以便能够就使用 TRE 进行 ML 模型开发做出明智的决策。
