Camouflage Learning

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Journal Title
Journal ISSN
Volume Title
Conference article in proceedings
Date
2021-05-25
Major/Subject
Mcode
Degree programme
Language
en
Pages
6
724-729
Series
2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events, PerCom Workshops 2021, IEEE international conference on pervasive computing and communications workshops
Abstract
Federated learning has been proposed as a concept for distributed machine learning which enforces privacy by avoiding sharing private data with a coordinator or distributed nodes. However, information on local data might be leaked through the model updates. We propose Camouflage learning, a machine learning scheme that distributes both the data and the model. Neither the distributed devices nor the coordinator is at any point in time in possession of the complete model. Furthermore, data and model are obfuscated during distributed model inference and distributed model training. Camouflage learning can be implemented with various Machine learning schemes.
Description
Funding Information: ACKNOWLEDGMENT We would like to acknowledge partial funding by the Academy of Finland Project ABACUS (ICT 2023). Publisher Copyright: © 2021 IEEE.
Keywords
Distributed machine learning, Internet of Things, Multi-key homomorphic encryption, Privacy
Other note
Citation
Sigg , S , Nguyen , L N & Ma , J 2021 , Camouflage Learning . in 2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events, PerCom Workshops 2021 . , 9431111 , IEEE international conference on pervasive computing and communications workshops , IEEE , pp. 724-729 , IEEE International Conference on Pervasive Computing and Communications Workshops , Kassel , Hesse , Germany , 22/03/2021 . https://doi.org/10.1109/PerComWorkshops51409.2021.9431111