Scaling Up Secure Processing, Anonymization And Generation Of Health Data For EU Cross Border Collaborative Research And Innovation

In SECURED, we offer a one stop collaboration hub (the SECURED Innohub) that can provide a secure and trusted environment for decentralized, cooperative processing of health data through SMPC techniques as well as generation of new, synthetic data and anonymization and anonymization assessment to health data providers and users. Our vision is to facilitate the broad adoption of health datasets across Europe by making the interconnection between EU health data hubs, the health data analytics research community, health application innovators (like Healthcare SMEs) as well as end users. The SECURED Innohub is offering apart from an SMPC and anonymization framework (with appropriate tools and services), the means to engage its members in the EU health data community by providing training and well as synthetic data to stem health data analysis research, medical education and an increase of the associated datasets volume and considerably reduce their bias. The SECURED vision is to kick start an EU cross-border health data collaboration ecosystem for data providers, data researchers and innovators that will be able to produce new AI based data analytics solutions and stem innovation.

Objective 1

Provide Secure Multiparty Computation (SMPC) schemes for AI based health data analytics tools along with appropriate enhancements to allow them to scale up in realistic health domain scenarios.

Objective 2

Provide advanced Anonymization on Health datasets and AI models as well as assess the anonymity level using de-anonymization/re-identification techniques

Objective 3

Provide an adaptable, configurable, and versatile Synthetic-data-generation tools and services for health/medical synthetic data including synthetic images

Objective 4

Creation and Management of the SECURED Privacy-Preserving and Robust Federated-Learning Infrastructure (SECURED Federation Infrastructure) for scalability support of SECURED Health-data-related services and tools. Assure that the created FL models and the anonymized data used for AI training are unbiased.

Objective 5

Integration of the SECURED components and infrastructure. Create the SECURED Innohub that can offer a framework of tools and services as well as training and knowledge for a broad range of researchers, EU Data hubs, Innovators and end-users

Objective 6

Evaluate the SECURED solution and associated technologies in terms of legal and ethical aspects. Assess the legal status of cross-country usage of anonymized and synthetic datasets as well as AI models.

Objective 7

Validation and Demonstration with four use cases that involve Cross-Border EU health data hubs offering anonymized data and offering privacy preserving data analysis as well as support training and education.

Objective 8

Provide a viable dissemination, exploitation and business model of the SECURED solution that will build momentum and support the continuation of a SECURED privacy preserving collaborative health data ecosystem beyond the end of the project

Latest News and Events


The SECURED concept and the latest developments in the project have been presented in the DATE 2024 Conference by the Project Coordinator prof. Francesco Regazzoni (UvA) at Valencia, Spain, 27 March 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.

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