Encryption-Friendly LLM Architecture
Donghwan Rho Taeseong Kim Minje Park Jung Woo Kim Hyunsik Chae Ernest K. Ryu Jung Hee Cheon
Large language models (LLMs) offer personalized responses based on user interactions, but this use case raises serious privacy concerns. Homomorphic encryption (HE) is a cryptographic protocol supporting arithmetic computations in encrypted states and provides a potential solution for privacy-preserving machine learning (PPML). However, the computational intensity of transformers poses challenges for applying HE to LLMs. In this work, we propose a modified HE-friendly transformer architecture with an emphasis on inference following personalized (private) fine-tuning. Utilizing LoRA fine-tuning and Gaussian kernels, we achieve significant computational speedups---6.94$\times$ for fine-tuning and 2.3$\times$ for inference---while maintaining performance comparable to plaintext models. Our findings provide a viable proof of concept for offering privacy-preserving LLM services in areas where data protection is crucial. Our code is available on GitHub.
Homomorphic Encryption, PPML, Encrypted Fine-tuning, Transformer Architecture