MPC Models
Three collaborators train one logistic-regression model over private rows. Arcium runs the MPC computation; Solana devnet records the hashes, accounts, and signatures an auditor needs to inspect the receipt.
How a training run happens.
The protocol does one thing: combine multiple private inputs into one model, and emit enough on-chain evidence that an outsider can inspect the receipt.
Private datasets stay local
Each collaborator keeps source rows in its own environment. The public never sees raw rows, names, labels, or feature values.
They publish commitments
A commitment is a signed fingerprint of a dataset. It proves which private input was used — without exposing the input itself.
MPC trains over encrypted inputs
The network jointly trains the model across private inputs. The computation uses the combined signal; individual rows stay hidden.
The trained model is verifiable
The final receipt stores linked accounts, signatures, and the public model commitment on devnet so anyone can rerun the checks later.
What each role can actually see.
Six artifacts move through the run. Switching role recolors every card by what the selected role can access; the artifact values come from the featured public receipt.
What lives on Solana devnet.
The chain holds the structure needed to re-run the proof: who joined, what schema they agreed to, which MPC circuit was used, and which public model came back. Hover or tap any field to read what it commits to.