Bridging the Gap: Differentially Private Equivariant Deep Learning for Medical Image Analysis
This addresses privacy concerns for sensitive medical imaging data, but it is incremental as it applies an existing method to a new domain.
The paper tackled the privacy-utility trade-off in differentially private medical image analysis by using steerable equivariant convolutional networks, resulting in remarkable accuracy gains that narrow the gap.
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.