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Securing your experience...
Securing your experience...
Enable hospitals and universities to collaborate on patient outcome research without sharing protected health data.
Good research requires diverse data. But hospitals can't share patient records across institutions due to HIPAA and patient privacy concerns. This means every institution trains on limited data, and cross-institutional research is blocked.
Each institution trains a model on its own patient data locally. Model updates are encrypted and aggregated centrally. The global model improves without any institution seeing others' raw data or revealing their own.
Hospital A trains a local model on its 100k patient records. Only model updates (gradients) are shared, encrypted.
Hospital B and University C do the same independently. No data is sent out of each institution.
A central aggregator combines all updates securely. The resulting global model is more accurate than any single institution's.
All institutions receive the improved global model and repeat.
Raw patient data never leaves the institution. Model updates are encrypted with differential privacy added. Even if updates are intercepted, individual patient records cannot be reverse-engineered.
No data movement, no centralization. Full audit trail of all cross-institutional updates and aggregations.
Noise is added to model updates to prevent privacy attacks. Privacy levels are configurable per institution.
Models trained on 10x to 100x more diverse data than single-institution models. Better for patient outcomes.
Full audit trail for research ethics boards and compliance audits. Document exactly which institutions contributed and when.