Private LLM Inference with Homomorphic Encryption [pdf] (www.vldb.org)

A recent tutorial introduces the concept of private large language model (LLM) inference using Fully Homomorphic Encryption (FHE), presented by researchers from the University of California, Santa Barbara. This emerging field aims to allow for sensitive data processing in cloud-based environments without revealing user inputs, a significant advancement given the increasing deployment of LLMs across various applications where privacy is a critical concern. The tutorial outlines the technical challenges still faced in implementing private LLM inference and suggests areas for future research, emphasizing systems like Thor, Tricycle, and Powerformer as notable examples. The significance of FHE in this context lies in its ability to perform computations directly on encrypted data, effectively eliminating the need for user data exposure to untrusted servers. While FHE promises strong privacy guarantees, its practical application in LLM inference is hindered by high computational costs and inefficiencies, particularly when approximating nonlinear layers common in LLM architectures. The tutorial highlights methodologies such as efficient encrypted matrix multiplications, and the use of polynomial approximations for non-linear functions, while addressing critical research questions that remain as barriers to broader adoption within the AI/ML community.