ProjectPrivateAIM – Privacy-Preserving Analytics in Medicine

Basic data

Privacy-Preserving Analytics in Medicine
01/04/2023 to 31/03/2027
Abstract / short description:
The goal of PrivateAIM is to develop a federated machine learning (ML) and data analytics platform for the Medical Informatics Initiative (MII), where analyses come to the data instead of data coming to the analyses. Methods enabling distributed data processing across the Data Integration Centers (DICs) set up by the MII are important for several reasons, including: (1) data from patients without consent may only be used if anonymity is provided, (2) federated technologies can help to connect the MII to other health data networks.
However, mechanisms currently established in the MII have significant limitations and are, for example, not suitable for complex ML and data science tasks. Moreover, federated platforms developed in other contexts (1) are complicated to deploy and operate, (2) feature a limited set of analytical or ML methods, (3) do not support modern privacy-enhancing technologies, and (4) lack scalability or maturity.
The PrivateAIM consortium, supported by all MII consortia, unites experts to develop the next-generation federated analytics and ML platform for the MII. The Federated Learning and Analytics Methods Platform (FLAME) will combine state-of-the-art federation methods with innovative privacy models for multimodal data. Using components to monitor and control privacy protection levels, these will be integrated into a distributed infrastructure, which can easily be adopted by the DICs.
The translation into practice will be facilitated through consideration of challenges on the intersection of technology and law, the development of concepts and documents for operation by hospital IT departments and coordination with ethics committees, as well as information security and data protection officers. FLAME will be evaluated with benchmark datasets and in real-world applications. Broad acceptance will be achieved by a multi-stage rollout concept. Availability as open-source software and collaboration with related projects will ensure sustainability.

Involved staff


Faculty of Science
University of Tübingen
Wilhelm Schickard Institute of Computer Science (WSI)
Department of Informatics, Faculty of Science
Center for Bioinformatics (ZBIT)
Quantitative Biology Center (QBIC)

Contact persons

Faculty of Medicine
University of Tübingen

Local organizational units

Medical Data Integration Center (meDIC)
Department for IT and Applied Medical Informatics (DITAMI)
Hospitals and clinical institutes, Faculty of Medicine
Institute for Translational Bioinformatics (TBI)
Department for IT and Applied Medical Informatics (DITAMI)
Hospitals and clinical institutes, Faculty of Medicine


Bonn, Nordrhein-Westfalen, Germany

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