A systematic review of privacy-preserving techniques for synthetic tabular health data - Tobias Hyrup; Anton D. Lautrup; Arthur Zimek; Peter Schneider-Kamp (Discover Data)
Abstract The amount of tabular health data being generated is rapidly increasing, which forces regulations to be put in place to ensure the privacy of individuals. However, the regulations restrict how data can be shared, limiting the research that can be conducted. Synthetic Data Generation (SDG) aims to solve that issue by generating data that mimics the statistical properties of real data without privacy concerns. Privacy is often assumed to exist in synthetic data without evaluating the model or the data. Accordingly, it is unclear how well various SDG methods preserve privacy. This review aims at uncovering how well privacy is preserved in tabular health data for different SDG methods and how privacy can be explicitly implemented in the SDG process. Relevant literature published from January 1, 2018–October 31, 2023 has been reviewed with a focus on privacy. The reported results and methods are compared to provide a standard frame of reference for future literature. The identified articles for the review total 32, with many explicitly implementing privacy constraints and all evaluating the privacy level. We found that methods for explicitly implementing privacy vary across generative models and identified a lack of standardization of privacy evaluation as an overarching theme. Our results show that SDG is a viable approach for ensuring patient confidentiality in tabular data. Still, to establish a solid foundation for future research, standardization of privacy evaluation is needed.