CC BY-NC-ND 4.0 · Methods Inf Med 2022; 61(S 01): e12-e27
DOI: 10.1055/s-0041-1740630
Original Article

Privacy-Preserving Artificial Intelligence Techniques in Biomedicine

Reihaneh Torkzadehmahani
1   Institute for Artificial Intelligence in Medicine and Healthcare, Technical University of Munich, Munich, Germany
Reza Nasirigerdeh
1   Institute for Artificial Intelligence in Medicine and Healthcare, Technical University of Munich, Munich, Germany
2   Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
David B. Blumenthal
3   Department of Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander University Erlangen-Nürnberg (FAU), Erlangen, Germany
Tim Kacprowski
4   Division of Data Science in Biomedicine, Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Medical School Hannover, Braunschweig, Germany
5   Braunschweig Integrated Centre of Systems Biology (BRICS), TU Braunschweig, Braunschweig, Germany
Markus List
6   Chair of Experimental Bioinformatics, Technical University of Munich, Munich, Germany
Julian Matschinske
7   E.U. Horizon2020 FeatureCloud Project Consortium
8   Chair of Computational Systems Biology, University of Hamburg, Hamburg, Germany
Julian Spaeth
7   E.U. Horizon2020 FeatureCloud Project Consortium
8   Chair of Computational Systems Biology, University of Hamburg, Hamburg, Germany
Nina Kerstin Wenke
7   E.U. Horizon2020 FeatureCloud Project Consortium
8   Chair of Computational Systems Biology, University of Hamburg, Hamburg, Germany
Jan Baumbach
7   E.U. Horizon2020 FeatureCloud Project Consortium
8   Chair of Computational Systems Biology, University of Hamburg, Hamburg, Germany
9   Institute of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark
› Author Affiliations
Funding The FeatureCloud project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No. 826078. This publication reflects only the authors' view and the European Commission is not responsible for any use that may be made of the information it contains. The work of J.B. and T.K. was also supported by the Horizon 2020 project REPO-TRIAL (No. 777111). M.L., T.K., and J.B. have further been supported by BMBF project Sys_CARE (01ZX1908A). M.L. and J.B. were also supported by BMBF project SyMBoD (01ZX1910D). J.B.'s contribution was also supported by his VILLUM Young Investigator grant (nr. 13154).


Background Artificial intelligence (AI) has been successfully applied in numerous scientific domains. In biomedicine, AI has already shown tremendous potential, e.g., in the interpretation of next-generation sequencing data and in the design of clinical decision support systems.

Objectives However, training an AI model on sensitive data raises concerns about the privacy of individual participants. For example, summary statistics of a genome-wide association study can be used to determine the presence or absence of an individual in a given dataset. This considerable privacy risk has led to restrictions in accessing genomic and other biomedical data, which is detrimental for collaborative research and impedes scientific progress. Hence, there has been a substantial effort to develop AI methods that can learn from sensitive data while protecting individuals' privacy.

Method This paper provides a structured overview of recent advances in privacy-preserving AI techniques in biomedicine. It places the most important state-of-the-art approaches within a unified taxonomy and discusses their strengths, limitations, and open problems.

Conclusion As the most promising direction, we suggest combining federated machine learning as a more scalable approach with other additional privacy-preserving techniques. This would allow to merge the advantages to provide privacy guarantees in a distributed way for biomedical applications. Nonetheless, more research is necessary as hybrid approaches pose new challenges such as additional network or computation overhead.

Note: This work was done during the time Reihaneh Torkzadehmahani was a member of the FeatureCloud consortium and affiliated with the Chair of Experimental Bioinformatics, Technical University of Munich.

Publication History

Received: 22 March 2021

Accepted: 18 September 2021

Article published online:
21 January 2022

© 2022. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (

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