Privacy-Preserving Artificial Intelligence Techniques in Biomedicine

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

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

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