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Review
. 2025 Nov 28;8(1):732.
doi: 10.1038/s41746-025-02112-0.

Protecting patient privacy in tabular synthetic health data: a regulatory perspective

Lisa Pilgram  1   2   3 , Haksoo Ko  4 , Adeline Tung  5 , Khaled El Emam  6   7
Affiliations
Review

Protecting patient privacy in tabular synthetic health data: a regulatory perspective

Lisa Pilgram et al. NPJ Digit Med. .

Abstract

Synthetic tabular data generation (SDG) is increasingly important in healthcare research and innovation while preserving patients' privacy. However, ethical concerns remain, primarily over residual privacy vulnerability and insufficient oversight. This review analyzes the only published SDG regulatory guidelines to date, from United Kingdom, Singapore, and South Korea. All emphasize privacy, acknowledging synthetic data is not inherently free from disclosure risks. Thresholds for sufficiently low risk are yet to be determined.

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Conflict of interest statement

Competing interests: At the time of writing KEE had shares in Aetion, which acquired his university spin-off company that develops SDG software. KEE was also the Scholar-in-Residence at the Office of the Information and Privacy Commissioner of Ontario at the time of writing.

Figures

Fig. 1
Fig. 1. Tabular Synthetic Data Generation for Healthcare.
There are multiple generative models that can be leveraged including AI/ML.
See this image and copyright information in PMC

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