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Exploring the Privacy-Preserving Properties of Word Embeddings: Algorithmic Validation Study
BACKGROUND: Word embeddings are dense numeric vectors used to represent language in neural networks. Until recently, there had been no publicly released embeddings trained on clinical data. Our work is the first to study the privacy implications of releasing these models. OBJECTIVE: This paper aims...
| Autores principales: | , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
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JMIR Publications
2020
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7391163/ https://www.ncbi.nlm.nih.gov/pubmed/32673230 http://dx.doi.org/10.2196/18055 |