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 harbours 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 worldwide first privacy-aware federated all-in-one approach.
Key strengths of FeatureCloud
Within this project, Egnosis is 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.