Paper 2024/1881

THOR: Secure Transformer Inference with Homomorphic Encryption

Abstract

As language models are increasingly deployed in cloud environments, privacy concerns have become a significant issue. To address this, we design THOR, a secure inference framework for transformer models on encrypted data. Specifically, we first propose new fast matrix multiplication algorithms based on diagonal-major order encoding and extend them to parallel matrix computation through the compact ciphertext packing technique. Second, we design efficient protocols for secure computations of four non-linear functions such as softmax, LayerNorm, GELU, and Tanh, by integrating advanced underlying approximation methods with tailored optimizations. Our matrix multiplication algorithms reduce the number of key-switching operations in the linear layers of the attention block in the BERT-base model by up to 14.5x, compared to the state-of-the-art HE-based secure inference protocol (Park et al., Preprint). Combined with cryptographic optimizations, our experimental results demonstrate that THOR provides secure inference for the BERT-base model with a latency of 10.43 minutes on a single GPU, while maintaining comparable inference accuracy on the MRPC dataset.