Scaling up multi-party computation, data anonymisation techniques, and synthetic data generation

il y a 17 jours

DNV

Grande entreprise

Créateur



Recherche partenariat

Recherche partenaire principal


HORIZON-HLTH-2022-IND-13-02

The proposals are expected to address all of the following areas:

  • Consolidate and scale up multi-party computation and data anonymisation techniques and synthetic data generation to support health technology providers, in particular SMEs.
  • Support the development of innovative unbiased AI based and distributed tools, technologies and digital solutions for the benefit of researchers, patients and providers of health services, while maintaining a high level of data privacy.
  • Advance the state-of-the-art of de-identification techniques, to tackle the challenge of anonymised datasets that can be traced back to individuals.
  • Develop innovative anonymisation techniques demonstrating that effective data quality and usefulness can be preserved without compromising privacy.
  • Explore and develop further the techniques of creating synthetic data, also dynamically on demand for specific use cases.
  • Widen the basis for GDPR-compliant research and innovation on health data.
  • Ensure wide uptake and scalability of the methodologies and tools developed, promote high standards of transparency and openness, going well beyond documentation and extending to aspects such as assumptions, architecture, code and any underlying data.

 Soins de santé
 Développement et coopération
 Biotechnologie
 Agenda numérique pour l'Europe
 Innovation & Recherche
 Horizon Europe
 Recherche
 Essais cliniques
 Intelligence artificielle

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