Papers › Nimbus: Secure and Efficient Two-Party Inference for Transformers

Nimbus: Secure and Efficient Two-Party Inference for Transformers

24 Nov 2024arXiv:2411.15707archive 2025-07-28

Zhengyi Li, Kang Yang, Jin Tan, Wen-jie Lu, Haoqi Wu, Xiao Wang, Yu Yu, Derun Zhao, Yancheng Zheng, Minyi Guo, Jingwen Leng

Transformer models have gained significant attention due to their power in machine learning tasks. Their extensive deployment has raised concerns about the potential leakage of sensitive information during inference. However, when being applied to Transformers, existing approaches based on secure two-party computation (2PC) bring about efficiency limitations in two folds: (1) resource-intensive matrix multiplications in linear layers, and (2) complex non-linear activation functions like 𝖦𝖤𝖫𝖴 and 𝖲𝗈𝖿𝗍𝗆𝖺𝗑. This work presents a new two-party inference framework 𝖭𝗂𝗆𝖻𝗎𝗌 for Transformer models. For the linear layer, we propose a new 2PC paradigm along with an encoding approach to securely compute matrix multiplications based on an outer-product insight, which achieves 2.9×∼12.5× performance improvements compared to the state-of-the-art (SOTA) protocol. For the non-linear layer, through a new observation of utilizing the input distribution, we propose an approach of low-degree polynomial approximation for 𝖦𝖤𝖫𝖴 and 𝖲𝗈𝖿𝗍𝗆𝖺𝗑, which improves the performance of the SOTA polynomial approximation by 2.9×∼4.0×, where the average accuracy loss of our approach is 0.08\% compared to the non-2PC inference without privacy. Compared with the SOTA two-party inference, 𝖭𝗂𝗆𝖻𝗎𝗌 improves the end-to-end performance of \bert{} inference by 2.7×∼4.7× across different network settings.

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