Benchmarking Differentially Private Residual Networks for Medical Imagery

• URL: http://arxiv.org/abs/2005.13099v5
• Date: Sat, 5 Sep 2020 02:25:06 GMT
• Title: Benchmarking Differentially Private Residual Networks for Medical Imagery
• Authors: Sahib Singh, Harshvardhan Sikka, Sasikanth Kotti, Andrew Trask
• Abstract summary: We compare two robust differential privacy mechanisms: Local-DP and DP-SGD. We evaluate how useful these theoretical privacy guarantees actually prove to be in the real world medical setting.
• Score: 2.4469484645516837
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper we measure the effectiveness of $\epsilon$-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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