SECURED will address the limitations which prevent the widespread use of SMPC and effective anonymisation (due to the lack of well understood and standardised data anonymisation methods for health data), with the main goal to scale up MPC, data anonymisation and synthetic data generation, by increasing the efficiency and improving security, focus on private and unbiased AI and data analytics, health-related hubs and data hubs, and cross-border cooperation.
The project will work on 4 use cases: Real-Time data analysis, Telemonitoring for children, Synthetic-data generation for education, and Access to genomic data.
Atos' contribution will be distributed in several project work packages focusing in data de-anonymization/re-identification, implementing toolbox libraries, privacy-preserving federated learning, technical specifications and architecture, framework knowledge base, CI/CD, requirements and evaluation. ARI will also give support to pilots, dissemination, exploitation, and trainings.
