Technology
Priviser is a privacy-preserving distributed computing protocol. Every value is split into random shares, the computation runs on the shares, and the answer is identical to what the pooled data would have given. This page sets it beside the alternatives, explains what that means for security and compliance, and shows what developers actually write.
Why Priviser
Priviser is a new category of privacy-preserving computation rather than a variant of federated learning, hardware enclaves or homomorphic encryption. When the market says confidential computing it usually means trusted execution environments, which move the trust into a chip and its vendor, whereas Priviser's confidentiality rests on mathematics alone.
| Data warehouse | Federated learning | Trusted execution (TEE) | Homomorphic encryption | Priviser | |
|---|---|---|---|---|---|
| Data stays with its owner | No, it must be ingested | Yes, but gradients leak | Yes, inside a vendor's chip | Yes | Yes, nothing informative crosses the wire |
| Whom you must trust | The platform | The aggregator | The hardware vendor | The maths, at a heavy price | The maths |
| Result vs plaintext | Identical | Approximate | Identical | Approximate, noise grows | Identical, verified |
| General computation | Yes, centralised | Training only | Predefined queries | Narrow circuits | Analytics, training, inference, joins |
| Cost | Integration | Bandwidth | Hardware lock-in | Orders of magnitude slower | Low, constant rounds |
A computation by one subscriber takes nothing away from the next, so the same dataset can serve any number of subscribers at once without being depleted or disclosed.
Because nobody reads anybody's data, competing firms subscribe to the same datasets and each combines them with its own context, and everybody's edge stays private.
A dataset is worth what it adds in combination with every other private context on the network, and that sum grows with each participant who joins.
Built on mathematics, not on trust.
Security and compliance
What leaves your node is a set of random shares that reveal nothing on their own, and no party, Priviser included, ever holds enough of them to reconstruct anything. Since no personal data is transferred, data-protection and sovereignty rules are not something you have to work around, because the computation never triggers them in the first place.
Whether a dataset is published or not, the raw data never leaves the contributor's environment, and withdrawing it leaves nothing behind anywhere.
Every task runs in an isolated container that can see only the registered dataset, under limited permissions.
There are no enclaves, no vendor in the trust chain and no attestation to break, because the guarantee is mathematical rather than physical.
Each participant checks every result, and code, models and outputs are protected in the same way as the inputs.
Developers
You submit a single file or a project with an entrypoint, bind its variables to datasets on any site, and choose a runtime, whether NumPy serial, NumPy parallel or Torch on GPU. Joins, statistics, regression, trees, support vector machines, Gaussian processes, multilayer perceptrons, convolutional networks and transformer components all run as secure operators underneath, so your code reads the way the plaintext version would.
Bring your own code, or start from curated algorithms for correlation, joint histograms and other common analyses.
One task can span many nodes, and both vertical and horizontal data splits are handled natively.
Everything runs in native float64 with exact non-linearities, so there is no fixed-point arithmetic and no polynomial approximation to erode your accuracy.
Next come large models on GPU clusters, agent orchestration, hardware-backed entropy, multi-cluster RoCE/RDMA and a native decentralised tensor.
Get in touch
A briefing takes about an hour and leaves you with a clear view of what your data could be worth on the network, and of what it would take to compute on the data you cannot reach today.