Related Experiment Videos
NoisyFlow: differentially private optimal transport using neural networks for secure biomedical data sharing across
Yunyang Li1, Nikhil Khandekar1, Skylar Wang1
1Department of Computer Science, Yale University, New Haven, CT 06511, United States.
Motivation:
Biomedical models improve when trained on data pooled across institutions, but sensitive patient records (e.g. genomics, clinical data, and medical images) are difficult to share due to privacy constraints. Moreover, data collected at different sites often have shifted distributions because of covariate differences (including batch effects), so privacy-preserving sharing alone cannot simply resolve cross-site mismatch. Methods that protect individuals while explicitly aligning distributions are needed to enable reliable multi-institutional analyses.
Results:
We present NoisyFlow, a three-stage differentially private framework for cross-institutional harmonization under distribution shift. In stage I, each site learns a differentially private flow-based generator of its local labeled distribution. In stage II, it learns a neural optimal transport map to a shared reference distribution. In stage III, a central server composes the released models to generate reference-aligned pseudo-data for downstream analysis without accessing raw records. Across four biomedical settings spanning single-cell genomics, histopathology, neurogenomics, and wearable sensing, NoisyFlow reduces distribution shift while preserving downstream utility under formal differential privacy guarantees.
Availability And Implementation:
The implementation of NoisyFlow is available at https://github.com/gersteinlab/NoisyFlow.
