Bridging the Gap: Differentially Private Equivariant Deep Learning for Medical Image Analysis. (arXiv:2209.04338v1 [eess.IV])

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.

analysis arxiv deep learning gap image medical