DP-RGMI Framework Analyzes Differential Privacy Effects in Medical Imaging Representation Geometry
A novel framework named Differential Privacy Representation Geometry for Medical Imaging (DP-RGMI) offers a systematic analysis of the impact of differential privacy on medical image evaluation. This method conceptualizes differential privacy as a modification of representation space, identifying performance decline as comprising two elements: encoder geometry and task-head usage. Geometry is evaluated by measuring representation displacement from its initial state and the spectral effective dimension, while task-head utilization is determined by contrasting linear-probe utility with overall performance. An analysis of more than 594,000 images from four chest X-ray datasets indicates that differential privacy consistently results in a utilization gap, despite linear separability being mostly preserved. The study, which clarifies the previously ambiguous mechanisms of privacy-related utility loss in medical imaging, was published on arXiv under identifier 2603.01098v2.
Key facts
- DP-RGMI framework interprets differential privacy as structured transformation of representation space
- Decomposes performance degradation into encoder geometry and task-head utilization
- Geometry quantified by representation displacement from initialization and spectral effective dimension
- Utilization measured as gap between linear-probe and end-to-end utility
- Analysis used over 594,000 images from four chest X-ray datasets
- DP consistently associated with utilization gap even when linear separability preserved
- Displacement and spectral dimension show non-monotonic behavior dependent on initialization
- Addresses unclear mechanism of privacy-induced utility loss in medical imaging
Entities
Institutions
- arXiv