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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| Autori principali: | , , , |
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| Natura: | Artigo |
| Lingua: | Inglês |
| Pubblicazione: |
Springer
2025-03-01
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| Serie: | Discover Data |
| Soggetti: | |
| Accesso online: | https://doi.org/10.1007/s44248-025-00022-w |
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