Privacy-preserving healthcare AI training
Privacy-preserving healthcare AI training should reduce the need to copy raw patient records into external AI infrastructure. Rapha Protocol approaches this with compute-to-data: the model workload moves into a controlled environment, while raw PHI stays inside the institution or device boundary.
Core requirements
- Data minimization: only the minimum approved output leaves the environment.
- Execution control: model containers are checked before running.
- Identity and consent: data access depends on policy and authorization.
- Proof metadata: commitments and receipts are auditable without revealing raw records.
- Legal review: production use needs contracts and compliance analysis.
How Rapha Protocol fits
Rapha Protocol coordinates researcher demand, secure API sessions, enterprise-node execution, proof metadata, and Polygon mainnet receipts. The protocol is designed so public infrastructure handles commitments and routing, not raw clinical payloads.
What this is not
Privacy-preserving architecture is not a substitute for clinical validation, HIPAA analysis, GDPR analysis, Taiwan PDPA analysis, IRB review, security review, business associate agreements, or data processing agreements where applicable.
Public Rapha Protocol demos are private-alpha and must use synthetic, seeded, or redacted data only.
Related pages: compute-to-data for clinical AI, how to train AI on real clinical data, and mainnet proof receipt.