A systematic review of privacy-preserving techniques for synthetic tabular health data

A systematic review of privacy-preserving techniques for synthetic tabular health 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) a...

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Bibliographic Details
Main Authors: Tobias Hyrup, Anton D. Lautrup, Arthur Zimek, Peter Schneider-Kamp
Format: Article
Language:English
Published: Springer 2025-03-01
Series:Discover Data
Subjects:
Synthetic data
Generative machine learning
Privacy
Privacy preservation
Health data
Tabular data
Online Access:https://doi.org/10.1007/s44248-025-00022-w
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https://doi.org/10.1007/s44248-025-00022-w

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