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Found 2 papers, 1 papers with code
Equivariant Differentially Private Deep Learning: Why DP-SGD Needs Sparser Models
We achieve such sparsity by design by introducing equivariant convolutional networks for model training with Differential Privacy.
Bridging the Gap: Differentially Private Equivariant Deep Learning for Medical Image Analysis
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.
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