Efficient Beaver Triple Generation for Privacy-preserving Collaborative Machine Learning
Paper
| Paper/Presentation Title | Efficient Beaver Triple Generation for Privacy-preserving Collaborative Machine Learning |
|---|---|
| Presentation Type | Paper |
| Authors | |
| Author | Tang, Zhaohui |
| Editors | Cho, Seongsoo |
| Journal or Proceedings Title | Proceedings of the 9th International Conference on Advanced Engineering and ICT-Convergence (ICAEIC-2022) |
| Journal Citation | 5 (2), pp. 14-20 |
| Article Number | 151 |
| Number of Pages | 7 |
| Year | 2022 |
| Place of Publication | Seoul, Korea |
| Web Address (URL) of Paper | https://ictaes.org/9th-international-conference/conference-program/ |
| Conference/Event | 9th 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. |
| Keywords | Collaborative machine learning, privacy-preerving machine learning, secure multi-party computation, Beaver triple, communication cost, Message Authentication Code (MAC) |
| ANZSRC Field of Research 2020 | 460402. Data and information privacy |
| Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
| Byline Affiliations | University of Southern Queensland |
| Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q7738/efficient-beaver-triple-generation-for-privacy-preserving-collaborative-machine-learning
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