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...

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Detalles Bibliográficos
Autores principales: Abdalla, Mohamed, Abdalla, Moustafa, Hirst, Graeme, Rudzicz, Frank
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2020
Materias:
Original Paper
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

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7391163/
https://www.ncbi.nlm.nih.gov/pubmed/32673230
http://dx.doi.org/10.2196/18055

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