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Bridging the Gap: Differentially Private Equivariant Deep Learning for Medical Image Analysis. (arXiv:2209.04338v1 [eess.IV])
Sept. 12, 2022, 1:20 a.m. | Florian A. Hölzl, Daniel Rueckert, Georgios Kaissis
cs.CR updates on arXiv.org arxiv.org
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.
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