Scaling Up Secure Processing, Anonymization And Generation Of Health Data For EU Cross Border Collaborative Research And Innovation
Latest NEws and Events
SECURED organizes a Summer School Tutorial
SECURED partners are organized a SECURED privacy preserving Tutorial in - Designing CPS from concepts to implementations, 18-22 September, 2023, Alghero, Sardinia (Italy) organized by UNISS
SECURED PROJECT AT HIPEAC CONFERENCE 2024
The work done in SECURED project regarding Fully Homomorphic Encryption was presented by prof. Paolo Palmieri (UCC partner) in hiPEAC conference 2024 on the tutorial entitled "Open Source Libraries and Components for Security " that took place in Munich on the 18th of January 2024.
Pilot 1 (Erasmus Medical Center): Real-Time tumor classification
Using the SECURED architecture, this pilot expects using Ultrasound and Brain imaging to generate novel insights related to the correlation of anatomically separate but functionally connected brain regions, confirmations of previous connectivities and discovery of new ones.
Pilot 2 (Paediatric Hospital Niño Jesús): Telemonitoring for children
Using the SECURED architecture, this pilot expects, in terms of prediction of bad evolution in oncology patients, the capability of using AI will foster the extension of telemedicine among paediatric patients, which can contribute significantly to meet goals 1, 3, 13, and 15 of 2030 Agenda for Sustainable Development.
Pilot 3 (Semmelwies University): Synthetic-data generation for education
Using the SECURED architecture, this pilot expects to facilitate movement between countries and statistical data collection while protecting individual privacy, generate large amounts of synthetic data guaranteed to be GDPR compliant, educate doctors get a quick overview of millions of cases due to machine learning data synthesis instead of generalising from a single cases and finally doctors integrate machine learning tools into their daily practice.
Pilot 4: (JCLRI) Access to Genomic Data
During this pilot, SECURED's federated learning system will allow researchers to train their models on all existing genetic data without the need of spending months preparing the paperwork to access it while, at the same time, preserving patient privacy, will transform the way genetics research is currently done.