Privacy Enhancing technology platform and applications
PryvX enables secure data collaboration through cutting-edge cryptography and a privacy-by-design approach.
Collaborate Without Sharing Data
Run computations on encrypted data—no raw data exchange. Stay compliant across sectors and jurisdictions.
Post-Quantum Cryptography
Built on advanced schemes like Fully Homomorphic Encryption to ensure future-proof security.
Flexible Deployment
Supports both centralized and federated (on-premise) setups.
No-Code AI & Analysis
Leverage LLMs for secure, no-code model building and collaborative insights.
Our PET-as-a-Service enables regulated industries (like BFSI, Healthcare, Telco, Govt, etc.) to collaborate on shared intelligence, analytics, and decision-making, without exposing raw data.
PryvX platform takes advantage of advance cryptography and distributed machine learning under Privacy Enhancing Technology framework to enable collaboration without sacrificing data privacy.
Use cases
Telecom - Bank fraud prevention
Gather fraud intelligence between telcos and banks using privacy-preserving data collaboration — detect SIM fraud, mule accounts, and identity theft without sharing raw data.
Private Credit Scoring
Enable secure credit assessments by combining financial and alternative data sources through encrypted collaboration — empowering accurate scoring even for thin-file or new-to-credit users.
Threat Intelligence sharing
Through the use of multi-party computation engine organizations can jointly analyze, share and identify emerging threats without compromising the confidentiality of their individual threat data.
IoT security
Federated learning enables each IoT device to locally train a model within its specific environment, and share the learnings to a global model that improves the overall security posture without exposing individual device data.
BT Group collaborates with PryvX
PryvX partnered with BT Group to showcase its privacy-enhancing platform at BT’s Innovation Hub in Adastral Park. This joint effort demonstrated how secure data collaboration can strengthen fraud and cybercrime prevention across financial and telecom sectors.
What is 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.