A Fully Private Pipeline for Deep Learning on Electronic Health Records

Edward Chou
Edward Chou
Thao Nguyen
Thao Nguyen
Josh Beal
Josh Beal
Li Fei-Fei
Li Fei-Fei

arXiv: Cryptography and Security, Volume abs/1811.09951, 2018.

Cited by: 1|Bibtex|Views72
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Other Links: dblp.uni-trier.de|academic.microsoft.com|arxiv.org

Abstract:

We introduce an end-to-end private deep learning framework, applied to the task of predicting 30-day readmission from electronic health records. By using differential privacy during training and homomorphic encryption during inference, we demonstrate that our proposed pipeline could maintain high performance while providing robust privacy...More

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