Benchmarking Differentially Private Residual Networks for Medical Imagery (ICML'20 Workshop on Health Systems-HSYS)

Abstract

In this paper we measure the effectiveness of e-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.

Experimental Setup

The experiments discussed in this section used an 18-Layer Residual Network(ResNet) previously trained to achieve convergence on the ImageNet task.
Input images passed to the deep neural network were scaled to 256 × 256 pixels, and normalized to 1. For performing the experiments we used Python 3.8.2 and PyTorch 1.4.0.

Datasets and Architectures

Dataset	ReseNet18
APTOS	✔️
Chest X-Rays	✔️

Paper & Poster

Paper: https://arxiv.org/pdf/2005.13099.pdf
Poster: https://manifoldcomputing.com/hsys_poster/

Citation

@article{singh2020benchmarking, title={Benchmarking Differentially Private Residual Networks for Medical Imagery}, author={Sahib Singh and Harshvardhan Sikka and Sasikanth Kotti and Andrew Trask}, journal={arXiv preprint arXiv:2005.13099}, year={2020} }