Powerformer: Efficient and High-Accuracy Privacy-Preserving Language Model with Homomorphic Encryption

We propose Powerformer, an efficient homomorphic encryption (HE)-based privacypreserving language model (PPLM) designed to reduce computational overhead while maintaining model performance.Powerformer incorporates three key techniques to optimize encrypted computations: 1) A novel distillation technique that replaces softmax and layer normalization with computationally efficient power and linear functions, ensuring no performance degradation while enabling seamless encrypted computation.2) A pseudo-sign composite approximation method that accurately approximates GELU and tanh functions with minimal computational overhead.3) A homomorphic matrix multiplication algorithm specifically optimized for Transformer models, enhancing efficiency in encrypted environments.By integrating these techniques, Powerformer based on the BERT-base model achieves a 45% reduction in computation time compared to the state-of-the-art HE-based PPLM without any loss in accuracy.