Exploring the Privacy-Preserving Properties of Word Embeddings: Algorithmic Validation Study
BackgroundWord 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. ObjectiveThis paper aims to d...
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| Main Authors: | , , , |
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| Format: | Book |
| Published: |
JMIR Publications,
2020-07-01T00:00:00Z.
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| Online Access: | Connect to this object online. |
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| Call Number: |
A1234.567 |
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| Copy 1 | Available |