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Adversarial interference and its mitigations in privacy-preserving collaborative machine learning

Nature Machine Intelligence volume 3, pages 749–758 (2021)Cite this article

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Abstract

Despite the rapid increase of data available to train machine-learning algorithms in many domains, several applications suffer from a paucity of representative and diverse data. The medical and financial sectors are, for example, constrained by legal, ethical, regulatory and privacy concerns preventing data sharing between institutions. Collaborative learning systems, such as federated learning, are designed to circumvent such restrictions and provide a privacy-preserving alternative by eschewing data sharing and relying instead on the distributed remote execution of algorithms. However, such systems are susceptible to malicious adversarial interference attempting to undermine their utility or divulge confidential information. Here we present an overview and analysis of current adversarial attacks and their mitigations in the context of collaborative machine learning. We discuss the applicability of attack vectors to specific learning contexts and attempt to formulate a generic foundation for adversarial influence and mitigation mechanisms. We moreover show that a number of context-specific learning conditions are exploited in similar fashion across all settings. Lastly, we provide a focused perspective on open challenges and promising areas of future research in the field.

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Fig. 1: Overview of the attacks.

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Acknowledgements

We thank the OpenMined community for its support. Funding: G.K. received funding from the Technical University of Munich, School of Medicine Clinician Scientist Programme (KKF), project reference H14. D.U. received funding from the Technical University of Munich/Imperial College London Joint Academy for Doctoral Studies. This research was supported by the UK Research and Innovation London Medical Imaging and Artificial Intelligence Centre for Value Based Healthcare. The funders played no role in the design of the study, the preparation of the manuscript or the decision to publish.

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  1. These authors contributed equally: Georgios Kaissis, Jonathan Passerat-Palmbach.

Authors and Affiliations

  1. Department of Computing, Imperial College London, London, UK

    Dmitrii Usynin, Daniel Rueckert, Ben Glocker, Georgios Kaissis & Jonathan Passerat-Palmbach

  2. Department of Diagnostic and Interventional Radiology, Technical University of Munich, Munich, Germany

    Dmitrii Usynin, Alexander Ziller, Marcus Makowski, Rickmer Braren & Georgios Kaissis

  3. OpenMined, Oxford, UK

    Dmitrii Usynin, Alexander Ziller, Georgios Kaissis & Jonathan Passerat-Palmbach

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

    Alexander Ziller, Daniel Rueckert & Georgios Kaissis

  5. ConsenSys Health, New York, NY, USA

    Jonathan Passerat-Palmbach

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Usynin, D., Ziller, A., Makowski, M. et al. Adversarial interference and its mitigations in privacy-preserving collaborative machine learning. Nat Mach Intell 3, 749–758 (2021). https://doi.org/10.1038/s42256-021-00390-3

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