Imagine you want to train a smart medical AI using patient data from five different countries, but you can't actually move the data because of strict privacy laws. Instead of moving the data to the AI, this system sends the AI to the data, learns from it locally, and only shares the 'lessons' learned. It's like a teacher visiting different classrooms to gather knowledge without ever taking the students out of their schools.
Medical organizations cannot share sensitive data for AI training due to privacy laws and security risks. This prevents the development of high-quality diagnostic tools that require large, diverse datasets from multiple countries.
A hybrid-cloud platform featuring privacy-aware network slices, a Zero Trust identity management system, and software components for federated machine learning.
If you are a hospital network dealing with strict GDPR and cross-border data laws — this project developed a delivery framework that allows secure AI training without moving sensitive patient records. This ensures compliance while improving diagnostic accuracy through shared intelligence.
If you are a security provider dealing with complex identity management across different organizations — this project developed a Zero Trust identity and access system. It provides continuous risk assessment to ensure only verified partners can access specific data slices.
If you are a cloud provider dealing with the need for automated service deployment — this project developed Zero Touch deployment and automatic life-cycle management. This allows for the rapid setup of privacy-preserving medical applications at the network edge.
Based on available project data, no specific pricing or cost details are provided as this was an EU-funded research project.
Yes, the project uses cloud-native solutions and open-source implementations specifically to ensure efficiency, scalability, and future adoption.
The project emphasizes open-source implementations to ensure future development and adoption, though specific license types are not listed.
It integrates standard compliance into a policy framework and provides tools for user rights, including opt-in/opt-out consent, portability, and the right to be forgotten.
Integration is achieved through privacy-aware Network Slices and a hybrid-cloud delivery framework (edge-central cloud).
The consortium is heavily weighted toward commercial application, with a 78% industry ratio consisting of 7 companies (including 4 SMEs). This high industrial presence, combined with 2 universities across 5 European countries, suggests the project is driven by market needs rather than pure academic curiosity.
Contact EURESCOM-EUROPEAN INSTITUTE FOR RESEARCH AND STRATEGIC STUDIES IN TELECOMMUNICATIONS GMBH in Germany.
Contact us to explore licensing for the privacy-preserving ML components.
