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
- 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)
- PDF:
- https://preview.aclanthology.org/autopr/2024.findings-acl.790.pdf