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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| 主要な著者: | , , , |
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| フォーマット: | Artigo |
| 言語: | Inglês |
| 出版事項: |
Springer
2025-03-01
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| シリーズ: | Discover Data |
| 主題: | |
| オンライン･アクセス: | https://doi.org/10.1007/s44248-025-00022-w |
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