End-to-end privacy preserving deep learning on multi-institutional medical imaging

Kaissis G, Ziller A, Passerat-Palmbach J, Ryffel T, Usynin D, Trask A, Lima I, Mancuso J, Jungmann F, Steinborn MM, Saleh A, Makowski M, Rueckert D, Braren R (2021)

Publication Type: Journal article

Publication year: 2021

Journal

Nature Machine Intelligence Nature Research

Book Volume: 3

Pages Range: 473-484

Journal Issue: 6

DOI: 10.1038/s42256-021-00337-8

Abstract

Using large, multi-national datasets for high-performance medical imaging AI systems requires innovation in privacy-preserving machine learning so models can train on sensitive data without requiring data transfer. Here we present PriMIA (Privacy-preserving Medical Image Analysis), a free, open-source software framework for differentially private, securely aggregated federated learning and encrypted inference on medical imaging data. We test PriMIA using a real-life case study in which an expert-level deep convolutional neural network classifies paediatric chest X-rays; the resulting model’s classification performance is on par with locally, non-securely trained models. We theoretically and empirically evaluate our framework’s performance and privacy guarantees, and demonstrate that the protections provided prevent the reconstruction of usable data by a gradient-based model inversion attack. Finally, we successfully employ the trained model in an end-to-end encrypted remote inference scenario using secure multi-party computation to prevent the disclosure of the data and the model.

Involved external institutions

Technische Universität München (TUM) DE Germany (DE) Klinikum Schwabing DE Germany (DE) Imperial College London / The Imperial College of Science, Technology and Medicine GB United Kingdom (GB)

How to cite

APA:

Kaissis, G., Ziller, A., Passerat-Palmbach, J., Ryffel, T., Usynin, D., Trask, A.,... Braren, R. (2021). End-to-end privacy preserving deep learning on multi-institutional medical imaging. Nature Machine Intelligence, 3(6), 473-484. https://doi.org/10.1038/s42256-021-00337-8

MLA:

Kaissis, Georgios, et al. "End-to-end privacy preserving deep learning on multi-institutional medical imaging." Nature Machine Intelligence 3.6 (2021): 473-484.

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