Data Sovereignty & Institutional Knowledge Protection
CENTAURON enables multi-institutional AI development without requiring clinical data or expert annotations to leave the contributing institution. The network is designed to protect data privacy and institutional knowledge assets by ensuring that slides, metadata and ground-truth annotations remain under the full control of their originators at all times.
To achieve this, CENTAURON employs a decentralized trust architecture combining:
- Local data custody: Whole-slide images and annotations remain on institutional infrastructure; no central repository is required.
- Client-side encryption: Sensitive annotation content is encrypted before leaving the browser; plaintext ground truth never becomes visible externally.
- Permissioned blockchain audit layer: All access grants and evaluation events are immutably recorded for verifiable accountability.
- Smart-contract-enforced access control: Computational requests only execute if cryptographically verified against agreed-upon usage terms.
- Selective metadata sharing: Institutions can expose general slide metadata globally while keeping diagnostic details and annotations encrypted or fully private.
In this model, data providers retain granular control over what is visible, what is encrypted and what remains entirely local. Models are distributed to participating institutions for evaluation, and only performance metrics return, ensuring sensitive data and annotation knowledge remain protected while still enabling collaborative validation and improvement of AI systems.
Sovereign Decentralization vs. Federated Learning
| Dimension | CENTAURON Node Model | Classic Federated Learning |
|---|---|---|
| Governance | Fully decentralized; no central coordinator | Central server or coordinating authority required |
| Who controls the data | Institution (Node owner) always retains full control | Central coordinator controls aggregation and orchestration |
| Data location | Data never leaves the institution; encrypted ground truth remains local | Data remains local but metadata/model updates always flow to a central point |
| Execution model | External AI models move to the data; executed in isolated containers | Local models trained, updates returned to central server for aggregation |
| Access transparency | All actions logged on blockchain; cryptographic enforcement | Trust placed in central coordinating entity |
| IP & annotation protection | Ground truth encrypted; never revealed to model providers | Labels/annotations often implicitly exposed through model gradients/updates |
| Membership model | Open to qualified institutions; identity-verified, peer-to-peer | Participation controlled by the central server/operator |
| Fault tolerance | No single point of failure | Central aggregator is a single point of failure |
| Regulatory alignment | Designed for medical compliance & auditability | Not inherently compliant or auditable in regulated healthcare environments |
| Value participation | Institutions keep value in data + annotations + execution | Central owner typically captures most value |
In short:
CENTAURON flips the federated learning model: Instead of institutions relying on a central aggregator, AI comes to the data, trust is programmatic, and every participant remains sovereign.