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The community explored to build private inference frameworks fortransformer-based large language models (LLMs) in a server-client setting,where the server holds the model parameters and the client inputs its privatedata (or prompt) for inference. However, these frameworks impose significantoverhead when the private inputs are forward propagated through the originalLLMs. In this paper, we show that substituting the computation- andcommunication-heavy operators in the transformer architecture withprivacy-computing friendly approximations can greatly reduce the privateinference costs while incurring very minor impact on model performance.Compared to state-of-the-art Iron (NeurIPS 2022), our privacy-computingfriendly model inference pipeline achieves a $5\times$ acceleration incomputation and an 80% reduction in communication overhead, while retainingnearly identical accuracy.

LLMs Can Understand Encrypted Prompt: Towards Privacy-Computing Friendly Transformers