An all-in-one platform to RUN, DEVELOP & PUBLISH federated & privacy-preserving machine learning algorithms

We envision a future in which doctors can easily and safely store, access, and manage primary medical patient data without risking a privacy breach and in which patients have full control and can change their minds at any time on what information they want to share or keep private.

The digital revolution, in particular big data mining, AI applications, and machine learning, offers new opportunities to transform healthcare. However, it also harbors risks to the safety of sensitive primary medical data such as patient information and raw data. In particular, data exchange over the internet is perceived as insurmountable, posing a roadblock that is hampering scientific progress and novel medical innovations that would only be possible by big data mining.

FeatureCloud tackles this perceived roadblock in an elegant way:
It is a transformative, pan-European research collaboration and AI development project which implements a software toolkit for substantially reducing cyber risks to healthcare infrastructure by employing the first worldwide privacy-aware federated all-in-one approach.

Key strengths of FeatureCloud:

  • No sensitive data is sent through any communication channels
  • Data is not stored in one central point of attack
  • Patients maintain full control over their data

The Role of Egnosis

Within FeatureCloud, Egnosis supports the consortium in WP7 by setting up intuitive web-based human-computer interfaces for patients (management of privacy rights) as well as for medical doctors and hospitals (project management), and developers (registration and testing of new apps) for the planned health informatics platform and app store.

As responsible for the system architecture and software development of the platform and app store, we are implementing a “privacy by design and architecture” approach to significantly increase cybersecurity with respect to data mining of patient data stored in hospitals and care service institutions.

“What would one say if one is more than happy to have an outstanding and trustworthy partner like Egnosis? They’ve never disappointed us, but more than that, they’ve also been consistently ahead in the field. Maybe exactly this.”

Prof. Dr. Jan Baumbach
FeatureCloud Project Coordinator, Director of the Institute for Computational Systems Biology University of Hamburg

Project data

Start date: 01 January 2019

Duration: 5 years

Horizon Europe Funding: €4.6 million

Egnosis’ Budget: €455,000

 

Project coordinator: University of Hamburg (Germany)

Participants:

  • University of Hamburg – Hamburg, Germany
  • concentris research management gmbh – Fürstenfeldbruck, Germany
  • Egnosis by Gnome Design Ltd. – Sfântu Gheorghe, Romania
  • Medizinische Universität Graz – Graz, Austria
  • Philipps Universität Marburg – Marburg, Germany
  • Research Institute AG & Co KG – Vienna, Austria
  • SBA Research gGmbH – Vienna, Austria
  • Syddansk Universitet – Odense, Denmark

 

Check it out on Cordis!

News and publications

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Check out the project’s main product, the all-in-one platform to run, develop & publish federated & privacy-preserving machine learning algorithms. You can read more about it in the official press release.

This project has received funding from the European Union’s Horizon2020 research and innovation program under grant agreement No 826078.
This website reflects only the authors’ view and the European Commission is not responsible for any use that may be made of the information it contains.
Source: https://featurecloud.eu/