Efficient Beaver Triple Generation for Privacy-preserving Collaborative Machine Learning

Paper


Tang, Zhaohui. 2022. "Efficient Beaver Triple Generation for Privacy-preserving Collaborative Machine Learning." Cho, Seongsoo (ed.) 9th International Conference on Advanced Engineering and ICT-Convergence (ICAEIC-2022). Jeju Island, Korea 13 - 15 Jul 2022 Seoul, Korea.
Paper/Presentation Title

Efficient Beaver Triple Generation for Privacy-preserving Collaborative Machine Learning

Presentation TypePaper
Authors
AuthorTang, Zhaohui
EditorsCho, Seongsoo
Journal or Proceedings TitleProceedings of the 9th International Conference on Advanced Engineering and ICT-Convergence (ICAEIC-2022)
Journal Citation5 (2), pp. 14-20
Article Number151
Number of Pages7
Year2022
Place of PublicationSeoul, Korea
Web Address (URL) of Paperhttps://ictaes.org/9th-international-conference/conference-program/
Conference/Event9th International Conference on Advanced Engineering and ICT-Convergence (ICAEIC-2022)
Event Details
9th International Conference on Advanced Engineering and ICT-Convergence (ICAEIC-2022)
Event Date
13 to end of 15 Jul 2022
Event Location
Jeju Island, Korea
Abstract

The privacy preservation issue in collaborative machine learning has attracted significant attentions from both academia and industry. Secure multi-party computation has been used widely to enhance the privacy preservation in collaborative machine learning while an efficient secure multi-party computation requires an efficient Beaver triple generation. In this paper, we propose an innovative scheme to efficiently generate Beaver triples, which in turn improves the efficiency of secure multi-party computation and thus facilitates an efficient implementation of privacy preservation in collaborative machine learning.

KeywordsCollaborative machine learning, privacy-preerving machine learning, secure multi-party computation, Beaver triple, communication cost, Message Authentication Code (MAC)
ANZSRC Field of Research 2020460402. Data and information privacy
Public Notes

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Byline AffiliationsUniversity of Southern Queensland
Institution of OriginUniversity of Southern Queensland
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https://research.usq.edu.au/item/q7738/efficient-beaver-triple-generation-for-privacy-preserving-collaborative-machine-learning

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