Sven Nebelung

The authors introduce DP-RGMI, a framework that treats differentially private training as a structured transformation of representation space and decomposes performance degradation into representation displacement, spectral effective dimension, and a utilization gap defined as the difference between linear-probe and end-to-end AUROC; across more than 594,000 chest X-rays from four datasets and three pretrained initializations (ImageNet, DINOv3, MIMIC-CXR), with PadChest as the primary dataset (110,525 frontal images, 22,045 test images), they find that strong privacy consistently leaves linear separability largely preserved while a utilization gap persists (G = 8.0 for ImageNet at ε = 1.0, 3.4 for MIMIC at ε = 0.7, 6.1 for DINOv3 at ε = 0.

The authors introduce DP-RGMI, a framework that treats differentially private training as a structured transformation of representation space and decomposes performance degradation into representation displacement, spectral effective dimension, and a utilization gap defined as the difference between linear-probe and end-to-end AUROC; across more than 594,000 chest X-rays from four datasets and three pretrained initializations (ImageNet, DINOv3, MIMIC-CXR), with PadChest as the primary dataset (110,525 frontal images, 22,045 test images), they find that strong privacy consistently leaves linear separability largely preserved while a utilization gap persists (G = 8.0 for ImageNet at ε = 1.0, 3.4 for MIMIC at ε = 0.7, 6.1 for DINOv3 at ε = 0.