A Fully Private Pipeline for Deep Learning on Electronic Health Records
arXiv: Cryptography and Security, Volume abs/1811.09951, 2018.
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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