Privacy-preserving distributed computing

Compute on data you could never share.

Priviser lets institutions train models, run analytics and score decisions on data that never leaves its owner's perimeter, with results identical to what they would get if the data had been pooled. Nothing is copied or handed over, and nobody has to trust a piece of hardware for that to hold.

Request a briefingSee how it works
app.priviser.com / datasets
CatalogSearch datasets, tags, sectors
DatasetSectorVolumeFreshnessCentrality
Regional hospital network · blood panels 2019–2026Healthcare2.4 TBlive●●●●○Subscribe
Urban mobility traces, anonymised, 14 citiesSmart city880 GBdaily●●●○○Subscribe
Card-not-present transactions, EU issuersFinancial6.1 TBreal time●●●●●Subscribe
Insurance claims history, motor, 2015–2026Insurance1.3 TBmonthly●●○○○Subscribe

The catalog holds descriptions only; sample rows are synthetic and the data itself never leaves the contributor's node.

Platform

Two roles, one network.

The platform sits between participants to coordinate, price and verify each computation, while the data itself stays on the contributor's own node from the first day to the last.

For data contributors

Your data earns without ever leaving.

You register the dataset on a node you run yourself and publish only its description to the catalog, so you keep full custody throughout. Each dataset is its own product, and the network prices it from how many subscribers link to it, how fresh it is and how large it is. You are paid when a subscriber takes computable access and again for every computation that runs on your data.

Become a contributor
http://localhost:8000
Local node · step 2 of 2
Start local serverRegister with your account
/data/registry/blood_panels_2019_2026
hospital-node-01 · online · container healthy

Only this path is ever mounted into execution containers.

For data subscribers

Run your own code on data you were never allowed to see.

You find datasets in the catalog, subscribe to the ones you need and submit a computation task, either a template algorithm or your own Python, bound to datasets across several sites and run on CPU or GPU. What comes back is the result rather than the rows, which you can then combine with what only your organisation knows.

Run a computation
app.priviser.com / tasks / new
New computation taskStart execution
Cross-site fraud scoring, logistic regression
Torch high performance (GPU, CUDA 12.4)
site_1.transactions → Bank A node · site_2.transactions → Bank B node · site_3.claims → Insurer node
from priviser import secure
X = secure.join(site_1, site_2, on="merchant_id")
model = secure.LogisticRegression().fit(X, site_3.label)
return model.score(X)          # result only, never rows

A task carries code, bindings and node configuration, and never the data itself.

How it works

The computation travels, the data stays.

Every value involved is split into random shares spread across the participating nodes, the computation runs on those shares, and the answer is reassembled only where it has been authorised. Here is what that looks like in practice.

Start a node where the data lives

You run one container on a host you own, inside your perimeter, with Docker or any OCI-compatible runtime. It needs no agents, no database connectors and no new openings in your network.

Register a dataset by path

You point the node at one approved folder, and that path is the only thing ever mounted into an execution container, with limited permissions and nothing else of your filesystem in reach.

Compute on shares

Inputs and intermediate values are split into additive shares that are statistically random on their own, so the nodes exchange nothing that could be read even if it were intercepted.

Receive the verified answer

The result is mathematically identical to what a plaintext computation would have produced, and every party checks it, so there is no accuracy to give up and no approximation to explain.

100×

Faster than encryption-based alternatives, at full precision.

Because Priviser computes in native float64 with exact non-linear operators and a constant, low number of communication rounds, it reaches speeds that encryption-based approaches cannot, and it does so without giving up a single digit of the result.

Solutions

Where data cannot move, Priviser computes.

Healthcare, financial services, government, enterprise groups and media all hold data that the law or the market keeps apart, and that is exactly where computing across it pays most.

See the solutions by industry

Healthcare

Train across hospitals without opening records.

Financial services

See the fraud that hides between banks.

Government

Sovereignty without isolation.

Enterprise

One view across divisions that will not share.

Security and compliance

Only your own process ever reads your data.

Your data is read only by a container you run, on a machine you own, inside your perimeter. What leaves it is random shares that reveal nothing on their own, and since no personal data is ever transferred, data-protection and sovereignty rules are never triggered by the computation.

Read the security overview

Data stays with its owner

Published or not, the raw data never leaves the contributor's environment.

No trusted hardware

No enclaves and no vendor in the trust chain, because the guarantee is mathematical.

Why Priviser

Others move the data or ask for trust.

Priviser is a new category of privacy-preserving computation rather than a variant of federated learning, hardware enclaves or homomorphic encryption, and its confidentiality rests on mathematics alone. The technology page sets the approaches side by side.

Compare the approaches
Non-rival

The same dataset serves everyone

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.

Tailored

Competitors join the same network

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.

Super-additive

Every dataset raises the value of the rest

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.

Get in touch

If your data cannot move, talk to us.

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.

Request a briefing