Privacy Enhancing technology platform and applications
PryvX's Privacy-Preserving Data Collaboration platform is designed to enable industries, i.e. telecom, financial services or other data-reliant industries to collaborate effectively to drive growth, reduce cost, risks, and prevent cyber crime.
PryvX platform takes advantage of advance cryptography and distributed machine learning under Privacy Enhancing Technology framework to enable collaboration without sacrificing sensitive data and data privacy.
Use cases
Telecom Fraud detection
Federated learning allows individual telecom operators to train models locally on their own data without sharing sensitive information. This enables them to create a global fraud detection model that benefits from the diverse data across different networks.
Banking fraud detction
Different banks can contribute insights from their financial transactions without sharing personal data. This helps in creating a more generalized and effective fraud detection model that can adapt to evolving fraud patterns while respecting privacy regulations.
Threat Intelligence sharing
Through the use of homomorphic encryption 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 its model based on its specific environment, contributing to a global model that improves the overall security posture without exposing individual device data.
BT Group collaborates with PryvX
Demoing a privacy enhancing platform at BT’s innovation showcase in Adastral Park, PryvX and BT are working together to help to combat cyber-crime and fraud in 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.