Comprehensive evaluation framework for synthetic tabular data in health: fidelity, utility and privacy analysis of generative models with and without privacy guarantees

The generation of synthetic tabular data has emerged as a key privacy-enhancing technology to address challenges in data sharing, particularly in healthcare, where sensitive attributes can compromise patient privacy. Despite significant progress, balancing fidelity, utility, and privacy in complex m...

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Principais autores:	Mikel Hernandez, Pablo A. Osorio-Marulanda, Mikel Catalina, Lorea Loinaz, Gorka Epelde, Naiara Aginako
Formato:	Artigo
Idioma:	Inglês
Publicado em:	Frontiers Media S.A. 2025-04-01
coleção:	Frontiers in Digital Health
Assuntos:	synthetic data generation generative models synthetic data fidelity synthetic data utility privacy risk measureattacks synthetic data evaluation
Acesso em linha:	https://www.frontiersin.org/articles/10.3389/fdgth.2025.1576290/full
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