Imperial College London
Portrait Picture

Dr Ben Glocker

Faculty of Engineering, Department of Computing

Professor in Machine Learning for Imaging
 
 
 
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Contact

 

+44 (0)20 7594 8334b.glocker Website CV

 
 
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Location

 

377Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Usynin:2021:10.1038/s42256-021-00390-3,
author = {Usynin, D and Ziller, A and Makowski, M and Braren, R and Rueckert, D and Glocker, B and Kaissis, G and Passerat-Palmbach, J},
doi = {10.1038/s42256-021-00390-3},
journal = {Nature Machine Intelligence},
pages = {749--758},
title = {Adversarial interference and its mitigations in privacy-preserving collaborative machine learning},
url = {http://dx.doi.org/10.1038/s42256-021-00390-3},
volume = {3},
year = {2021}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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.
AU  - Usynin,D
AU  - Ziller,A
AU  - Makowski,M
AU  - Braren,R
AU  - Rueckert,D
AU  - Glocker,B
AU  - Kaissis,G
AU  - Passerat-Palmbach,J
DO  - 10.1038/s42256-021-00390-3
EP  - 758
PY  - 2021///
SN  - 2522-5839
SP  - 749
TI  - Adversarial interference and its mitigations in privacy-preserving collaborative machine learning
T2  - Nature Machine Intelligence
UR  - http://dx.doi.org/10.1038/s42256-021-00390-3
UR  - https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000696824400004&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=a2bf6146997ec60c407a63945d4e92bb
UR  - https://www.nature.com/articles/s42256-021-00390-3
UR  - http://hdl.handle.net/10044/1/109801
VL  - 3
ER  -
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