# Bridging the Gap: Differentially Private Equivariant Deep Learning for Medical Image Analysis

arXiv: https://arxiv.org/abs/2209.04338 · HTML: https://mlsanity.com/paper/2209.04338/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 10 references that are in the map.
- First posted on arXiv 9 Sep 2022.

## Builds on (most cited first, 8 of 8)

- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Group Equivariant Convolutional Networks](https://mlsanity.com/paper/1602.07576/) (2016)
- [MedMNIST v2 -- A large-scale lightweight benchmark for 2D and 3D biomedical image classification](https://mlsanity.com/paper/2110.14795/) (2021)
- [General E(2)-Equivariant Steerable CNNs](https://mlsanity.com/paper/1911.08251/) (2019)
- [Steerable CNNs](https://mlsanity.com/paper/1612.08498/) (2016)
- [Differentially Private Learning Needs Better Features (or Much More Data)](https://mlsanity.com/paper/2011.11660/) (2020)
- [Smooth Loss Functions for Deep Top-k Classification](https://mlsanity.com/paper/1802.07595/) (2018)
- [Differentially private training of residual networks with scale normalisation](https://mlsanity.com/paper/2203.00324/) (2022)

## Built on by (most cited first, 2 of 2)

- [Differentially Private Image Classification by Learning Priors from Random Processes](https://mlsanity.com/paper/2306.06076/) (2023)
- [End-to-End Differential Privacy in Training Deep Neural Network Classifiers](https://mlsanity.com/paper/2607.19580/) (2026)

## Abstract (arXiv)

Machine learning with formal privacy-preserving techniques like Differential Privacy (DP) allows one to derive valuable insights from sensitive medical imaging data while promising to protect patient privacy, but it usually comes at a sharp privacy-utility trade-off. In this work, we propose to use steerable equivariant convolutional networks for medical image analysis with DP. Their improved feature quality and parameter efficiency yield remarkable accuracy gains, narrowing the privacy-utility gap.