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Benchmarking Differentially Private Residual Networks for Medical Imagery

27 May 2020
Sahib Singh
Harshvardhan Digvijay Sikka
Sasikanth Kotti
Andrew Trask
ArXiv (abs)PDFHTML
Abstract

In this paper we measure the effectiveness of ϵ\epsilonϵ-Differential Privacy (DP) when applied to medical imaging. We compare two robust differential privacy mechanisms: Local-DP and DP-SGD and benchmark their performance when analyzing medical imagery records. We analyze the trade-off between the model's accuracy and the level of privacy it guarantees, and also take a closer look to evaluate how useful these theoretical privacy guarantees actually prove to be in the real world medical setting.

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