Privacy-Preserving Artificial Intelligence Techniques in Biomedicine

Reihaneh Torkzadehmahani; Reza Nasirigerdeh; David B. Blumenthal; Tim Kacprowski; Markus List; Julian Matschinske; Julian Spaeth; Nina Kerstin Wenke; Jan Baumbach

doi:10.1055/s-0041-1740630

Methods of Information in Medicine, Table of Contents

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

Authors

Reihaneh Torkzadehmahani

¹Institute for Artificial Intelligence in Medicine and Healthcare, Technical University of Munich, Munich, Germany
Reza Nasirigerdeh

¹Institute for Artificial Intelligence in Medicine and Healthcare, Technical University of Munich, Munich, Germany

²Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
David B. Blumenthal

³Department of Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander University Erlangen-Nürnberg (FAU), Erlangen, Germany
Tim Kacprowski

⁴Division of Data Science in Biomedicine, Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Medical School Hannover, Braunschweig, Germany

⁵Braunschweig Integrated Centre of Systems Biology (BRICS), TU Braunschweig, Braunschweig, Germany
Markus List

⁶Chair of Experimental Bioinformatics, Technical University of Munich, Munich, Germany
Julian Matschinske

⁷E.U. Horizon2020 FeatureCloud Project Consortium

⁸Chair of Computational Systems Biology, University of Hamburg, Hamburg, Germany
Julian Spaeth

⁷E.U. Horizon2020 FeatureCloud Project Consortium

⁸Chair of Computational Systems Biology, University of Hamburg, Hamburg, Germany
Nina Kerstin Wenke

⁷E.U. Horizon2020 FeatureCloud Project Consortium

⁸Chair of Computational Systems Biology, University of Hamburg, Hamburg, Germany
Jan Baumbach

⁷E.U. Horizon2020 FeatureCloud Project Consortium

⁸Chair of Computational Systems Biology, University of Hamburg, Hamburg, Germany

⁹Institute of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark

Abstract

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.

Keywords

privacy-preserving AI techniques - federated learning - biomedicine

Full Text

References

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