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Privacy-Preserving Deep Learning for the Detection of Protected Health Information in Real-World Data: Comparative Evaluation
BACKGROUND: Collaborative privacy-preserving training methods allow for the integration of locally stored private data sets into machine learning approaches while ensuring confidentiality and nondisclosure. OBJECTIVE: In this work we assess the performance of a state-of-the-art neural network approa...
| Autores principales: | , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
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JMIR Publications
2020
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| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7238077/ https://www.ncbi.nlm.nih.gov/pubmed/32369025 http://dx.doi.org/10.2196/14064 |