BumbleBee: Secure Two-party Inference Framework for Large Transformers
Wenjie Lu, Zhicong Huang, Zhen Gu, Jingyu Li, Jian Liu, Cheng Hong, Kui Ren, Tao Wei, Wenguang Chen · 2025
Large transformer-based models have realized stateof-the-art performance on lots of real-world tasks such as natural language processing and computer vision.However, with the increasing sensitivity of the data and tasks they handle, privacy has become a major concern during model deployment.In this work, we focus on private inference in two-party settings, where one party holds private inputs and the other holds the model.We introduce BumbleBee, a fast and communication-friendly twoparty private transformer inference system.Our contributions are three-fold: First, we propose optimized protocols for matrix multiplication, which significantly reduce communication costs by 80% -90% compared to previous techniques.Secondly, we develop a methodology for constructing efficient protocols tailored to the non-linear activation functions employed in transformer models.The proposed activation protocols have realized a significant enhancement in processing speed, alongside a remarkable reduction in communication costs by 80% -95% compared with two prior methods.Lastly, we have performed extensive benchmarks on five transformer models.BumbleBee demonstrates its capability by evaluating the LLaMA-7B model, generating one token in approximately 8 minutes using CPUs.Our results further reveal that BumbleBee outperforms Iron (NeurIPS22) by over an order of magnitude and is three times faster than BOLT (Oakland24) with one-tenth communication.