Our consortium member IDSA is on a mission to bring sovereignty and trust to data ecosystems while the Reference Architecture Model (IDS-RAM) is offering a framework to leverage more data in a trusted way.

But how could it be executed in highly constrained environments? What can the technology could offer when data cannot be moved or disclosed at all? What are the State-of-the-Art solutions that could meet the highest standards for the health sector or in the Industry when it comes to sensitive information?

Experts from the MUSKETEER project – “Machine learning to augment shared knowledge in federated privacy-preserving scenarios” – will present and discuss their efforts to bring more privacy preserving technologies to help this demand of trustworthiness in data sharing ecosystems.

MUSKETEER is developing an industrial data platform with scalable algorithms for federated and privacy-preserving machine learning techniques, detection and mitigation of adversarial attacks, and a rewarding model capable of fairly monetizing datasets according to the real data value.