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:
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.”
01 January 2019
9 institutions from 5 countries
Horizon Europe Funding
This project has received funding from the European Union’s Horizon Europe research and innovation programme under grant agreement No. 826078.
Meet the members on the project website.
The project’s main product, the all-in-one platform to run, develop & publish federated & privacy-preserving machine learning algorithms, can be found here. You can read more about it in the official press release.
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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.