OpenMined

Each hospital deploys a Syft Domain node.

Data is indexed locally.

Researcher sends a PyTorch model to all nodes.

Nodes train on local data.

Aggregated model updates are merged via SMPC.

Banks connect to a private PyGrid network.

Transactions are processed locally.

Shared insights on fraud signatures are exchanged via Differential Privacy.

Local models are updated simultaneously.

Suppliers encrypt data using Homomorphic Encryption.

A central aggregator runs optimization queries on encrypted data.

Decrypted results show delays without revealing supplier inventory.

User phone acts as a Syft node.

Insurance algorithm runs locally on the device.

A 'risk score' is returned to the insurer without sending raw GPS or heart rate data.

Data is queried via Syft.

Differential Privacy noise is added to the results.

A privacy budget prevents too many queries on the same individual group.

Ad clicks are recorded locally.

Purchase data is recorded locally.

SMPC matches the two events without either party seeing the user's identity.

Models are trained on classified imagery locally.

Model gradients are sanitized and shared.

Global model improves without exposing specific image assets.