KNT applies key-conditioned nonlinear obfuscation to split-inference features, cutting re-identification AUC from 0.635 to 0.586 with 0.15 ms overhead and under 1 pp accuracy loss.
Medical imaging deep learning with differential privacy.Scientific Reports, 11:13524
2 Pith papers cite this work, alongside 155 external citations. Polarity classification is still indexing.
2
Pith papers citing it
155
external citations · OpenAlex
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Evaluating DPSGD clipping methods on medical segmentation shows prior assumptions fail in this domain, but adding morphological refinement and an adaptive DP-Morph variant improves utility under privacy constraints.
citing papers explorer
-
Keyed Nonlinear Transform: Lightweight Privacy-Enhancing Feature Sharing for Medical Image Analysis
KNT applies key-conditioned nonlinear obfuscation to split-inference features, cutting re-identification AUC from 0.635 to 0.586 with 0.15 ms overhead and under 1 pp accuracy loss.
-
From Gradient Clipping to Structural Refinement: Improving DPSGD for Medical Image Segmentation
Evaluating DPSGD clipping methods on medical segmentation shows prior assumptions fail in this domain, but adding morphological refinement and an adaptive DP-Morph variant improves utility under privacy constraints.