# Benchmarking Differentially Private Residual Networks for Medical Imagery

arXiv: https://arxiv.org/abs/2005.13099 · HTML: https://mlsanity.com/paper/2005.13099/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 May 2020.

## Built on by (most cited first, 3 of 3)

- [DP-SGD vs PATE: Which Has Less Disparate Impact on Model Accuracy?](https://mlsanity.com/paper/2106.12576/) (2021)
- [Benchmarking Differential Privacy and Federated Learning for BERT Models](https://mlsanity.com/paper/2106.13973/) (2021)
- [When Differential Privacy Meets Interpretability: A Case Study](https://mlsanity.com/paper/2106.13203/) (2021)

## Abstract (arXiv)

In this paper we measure the effectiveness of ε-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.