SecFormer: Fast and Accurate Privacy-Preserving Inference for Transformer Models via SMPC

Jinglong Luo, Yehong Zhang, Zhuo Zhang, Jiaqi Zhang, Xin Mu, Hui Wang, Yue Yu, Zenglin Xu


Anthology ID:
2024.findings-acl.790
Volume:
Findings of the Association for Computational Linguistics: ACL 2024
Month:
August
Year:
2024
Address:
Bangkok, Thailand
Editors:
Lun-Wei Ku, Andre Martins, Vivek Srikumar
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
13333–13348
Language:
URL:
https://aclanthology.org/2024.findings-acl.790
DOI:
10.18653/v1/2024.findings-acl.790
Bibkey:
Cite (ACL):
Jinglong Luo, Yehong Zhang, Zhuo Zhang, Jiaqi Zhang, Xin Mu, Hui Wang, Yue Yu, and Zenglin Xu. 2024. SecFormer: Fast and Accurate Privacy-Preserving Inference for Transformer Models via SMPC. In Findings of the Association for Computational Linguistics: ACL 2024, pages 13333–13348, Bangkok, Thailand. Association for Computational Linguistics.
Cite (Informal):
SecFormer: Fast and Accurate Privacy-Preserving Inference for Transformer Models via SMPC (Luo et al., Findings 2024)
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PDF:
https://aclanthology.org/2024.findings-acl.790.pdf