Plenary meeting PAROMA-MED
Project PAROMA-MED plenary meeting took place on 4th & 5th May 2023 in Genova. It was organized and hosted by Ericsson at their premise in hybrid mode. This plenary meeting […]
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PAROMA-MED at Digital Health Event in Treviso
On 23rd Feb Project coordinator Alessandro Bassi, EURESCOM presented project PAROMA-MED – Privacy Aware and Privacy Preserving Distributed and Robust Machine Learning for Medical Applications at the event digital health, […]
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PAROMA-MED concept and planned approach presented at IEEE Future Networks World Forum 2022
On 12 October 2022, PAROMA-MED concept and planned approach was presented during IEEE Future Networks World Forum in the “S6: Symposium on Security for 5G and Future Networks” in the […]
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PAROMA-MED
On 1st & 2nd Dec 2022, consortium PAROMA-MED met in Athens,Greece for its 1st Plenary. Presentations & discussions in this Plenary meeting were about the Work Packages and deliverables status, […]
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PAROMA-MED builds a unique platform
Project PAROMA-MED builds platform to develop, validate and evaluate a platform-based (edge – central cloud) delivery framework for privacy- and security – assured services and applications in federative cross-border environments. […]
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Privacy Aware and Privacy Preserving Distributed and Robust Machine Learning for Medical Applications
Machine learning can lead to great advancements with respect to digital services and applications in the field of medical sector, the training process based on real medical patient data is blocked by the fact that uncontrolled access to and exposure of such assets is not allowed by data protection legislation. The EU-funded PAROMA-MED project aims to develop novel technologies, tools, services and architectures for patients, health professionals, data scientists and health domain businesses so that they will be able to interact in the context of data and ML federations according to legal constraints and with complete respect to data owners’ rights from privacy protection to fine grained governance, without performance and functionality penalties of ML/AI workflows and applications.
