We empower organizations to collaborate and innovate with data securely and responsibly

Diagram showing how the PryvX platform connects data collaborators, our products, and outcomes through privacy-enhancing technology

One platform for privacy-preserving collaboration

PryvX enables secure data collaboration through cutting-edge cryptography and a privacy-by-design solutions — connecting data providers and consumers so regulated sectors can collaborate on analytics without exposing raw data.

Computation on Encrypted Data

Run analytics and models directly on encrypted data — no raw data exchange between parties, ever.

Post-Quantum Cryptography

Fully Homomorphic Encryption and additive secret shares, built on cryptographic primitives designed to withstand next-generation threats.

Cloud-Agnostic Deployment

Deploy centralized or federated across AWS, GCP, Azure or on-prem — without re-architecting your existing stack.

No-Code AI & Analysis

Non-technical teams run privacy-preserving analysis and LLM-powered workflows on sensitive data without writing a line of code.

How PET-as-a-Service is delivered

PET-as-a-Service isn't just access to cryptographic primitives — it's a full integration, deployment and support model, built so regulated teams can ship a privacy-preserving pipeline in weeks rather than commissioning a research project.

1

Assess

We map your data flows and regulatory constraints to the right combination of PETs for the job — no one-size-fits-all default.

2

Integrate

Connect via SDK or API against your existing data warehouse and ML stack — most teams ship a first privacy-preserving pipeline in weeks, not quarters.

3

Deploy

Run centralized or federated, across AWS, GCP, Azure, on-prem, or fully air-gapped — the deployment model follows your constraints, not ours.

4

Operate

Ongoing engineering support, monitoring, and audit-ready compliance reporting are part of the engagement from day one, not bolted on after.

Core Technologies

FL

Federated Learning

Federated Learning is a machine learning technique that enables multi parties to train a model locally on its individual data, and share only the learnings to build a global model.

Use case

Joint fraud-detection or credit-risk models across banks and telcos without centralizing customer records.

Learn more about FL