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

Florian Alexander Hölzl, Daniel Rueckert, Georgios Kaissis

Keywords: Differential Privacy, Equivariant Convolutions, Steerable Kernels

TL;DR: We narrow the privacy-utility gap of differential private deep learning for medical image analysis by using steerable equivariant convolutional networks.

Abstract:

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.