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

Files associated with this item cannot be displayed due to copyright restrictions.

Byline AffiliationsUniversity of Southern Queensland
Institution of OriginUniversity of Southern Queensland
Permalink -

https://research.usq.edu.au/item/q7738/efficient-beaver-triple-generation-for-privacy-preserving-collaborative-machine-learning

Log in to edit
  • 127
    total views
  • 9
    total downloads
  • 4
    views this month
  • 0
    downloads this month

Export as

Related outputs

Lightweight federated learning for STIs/HIV prediction
Nguyen, Thi Phuoc Van, Yang, Wencheng, Tang, Zhaohui, Xia, Xiaoyu, Mullens, Amy B., Dean, Judith A. and Li, Yan. 2024. "Lightweight federated learning for STIs/HIV prediction." Scientific Reports. 14 (1). https://doi.org/10.1038/s41598-024-56115-0
A Review of Homomorphic Encryption for Privacy-Preserving Biometrics
Yang, Wencheng, Wang, Song, Cui, Hui, Tang, Zhaohui and Li, Yan. 2023. "A Review of Homomorphic Encryption for Privacy-Preserving Biometrics." Sensors. 23 (7). https://doi.org/10.3390/s23073566
Federated Learning with Real-world Datasets: Compliance with the Privacy Act
Tang, Zhaohui and Keoh, Sye Loong. 2022. "Federated Learning with Real-world Datasets: Compliance with the Privacy Act." Cho, Seongsoo (ed.) 9th International Conference on Advanced Engineering and ICT-Convergence (ICAEIC-2022). Jeju Island, Korea 13 - 15 Jul 2022 Seoul, Korea.
Secret sharing-based IoT text data outsourcing: a secure and efficient scheme
Tang, Zhaohui. 2021. "Secret sharing-based IoT text data outsourcing: a secure and efficient scheme." IEEE Access. 9, pp. 76908 -76920. https://doi.org/10.1109/ACCESS.2021.3075282
An Efficient Scheme to Secure Data Provenance in Home Area Networks
Tang, Zhaohui and Keoh, Sye Loong. 2020. "An Efficient Scheme to Secure Data Provenance in Home Area Networks." IEEE 3rd 5G World Forum (5GWF 2020). Bangalore, India 10 - 12 Sep 2020 United States. https://doi.org/10.1109/5GWF49715.2020.9221402
Privacy Preserving Computation in Home Loans Using the FRESCO Framework
Chan, Fook Mun, Xu, Quanqing, Seah, Hao Jian, Keoh, Sye Loong, Tang, Zhaohui and Aung, Khin Mi Mi. 2019. "Privacy Preserving Computation in Home Loans Using the FRESCO Framework." Kacprzyk, Janusz (ed.) 2018 Future of Information and Communication Conference (FICC 2018). Singapore 05 - 06 Apr 2018 Switzerland. https://doi.org/10.1007/978-3-030-03405-4_7
Authentication and Integrity Protection for Real-time Cyber-Physical Systems
Keoh, Sye Loong, Tan, Heng Chuan and Tang, Zhaohui. 2020. "Authentication and Integrity Protection for Real-time Cyber-Physical Systems." Tian, Yu-Chu and Levy, David Charles (ed.) Handbook of Real-Time Computing. Singapore. Springer. pp. 1-22