A privacy preserving Health Data Space approach for federated machine learning

In this paper, we present the key concepts, principles and architecture approach of the EU Project PAROMA-MED. The project works on a novel hybrid cloud approach, with an elevated role of the edge, that introduces and utilizes advanced privacy preserving solutions. The overall objective is to accelerate the adoption of personal data federation on top of which Federated ML scenarios can be easily executed. The approach focuses on the establishment of high degree of trust between data owner and data management infrastructure so that consent in data processing is given by means of functional and enforceable options applicable at all levels of workloads and processes