PAROMA-MED held its 7th Plenary Meeting in Athens, Greece, hosted by UBITECH partner

PAROMA-MED partners met in Athens between the 26th and 28th of February 2024 to hold their 7th Plenary Meeting hosted and organised by UBITECH. It was three days of collaboration, […] 

PAROMA-MED at European Cyber Week

From 21-23 November 2023, communication in ECW – European Cyber Week, was organized in Rennes . During this conference, PAROMA-MED partners from Thales, Mohammed Lansari presented watermarking in the context […] 

6th Plenary PAROMA-MED

From 13-15November 2023, PAROMA-MED consortium came together, this time in Paris (France) for the 6th Plenary meeting which was organized by our partners from Thales. The plenary was organized in […] 

Publication: Machine Learning & knowledge extraction

A new publication from the project PAROMA-MED is published by the partners Thales/IMT  on When Federated Learning meets Watermarking: A Comprehensive Overview of Techniques for Intellectual Property Protection, published in […] 

Press Release PAROMA-MED

On 2nd of Oct Press release from the project PAROMA-MED was released and it provided an overview on the Project´s goal and progress. The project has entered its second year […] 

Plenary meeting PAROMA-MED

From 4-5 July 2023, PAROMA-MED consortium came together in Vienna (Austria) for the Plenary meeting which was organized by our partners from 6GHI. The plenary was organized in hybrid mode. […] 

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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.

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