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Privacy Enhancing technology applications

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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.

PryvX

is a platform that enables collaboration to combat cybercrime and fraud while preserving privacy, by leveraging privacy enhancing technologies, such as federated learning.

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 learning's to build a global model. 

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