Secured

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

SECURED Pilots

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.