Sharing is CAIRing: Characterizing principles and assessing properties of universal privacy evaluation for synthetic tabular data

Data sharing is a necessity for innovative progress in many domains, especially in healthcare. However, the ability to share data is hindered by regulations protecting the privacy of natural persons. Synthetic tabular data provide a promising solution to address data sharing difficulties but does no...

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Autors principals:	Tobias Hyrup, Anton Danholt Lautrup, Arthur Zimek, Peter Schneider-Kamp
Format:	Artigo
Idioma:	Inglês
Publicat:	Elsevier 2024-12-01
Col·lecció:	Machine Learning with Applications
Matèries:	Privacy Privacy evaluation Synthetic data Data sharing Tabular data Machine learning
Accés en línia:	http://www.sciencedirect.com/science/article/pii/S2666827024000847
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