Cryptography-based privacy-preserving large language models: a lifecycle survey of frameworks, methods, and future directions

The rapid development of Transformer-based large language models (LLMs) has made them one of the most critical technological infrastructures in modern society. However, this rapid deployment has transformed the risk of privacy breaches from a theoretical concern into a systemic threat spanning the entire lifecycle of LLMs. These risks continually challenge existing data compliance and regulatory frameworks, directly limiting the large-scale adoption of LLMs in highly sensitive and heavily regulated industries. Cryptographic technologies, such as fully homomorphic encryption (FHE) and secure multi-party computation (MPC), have garnered significant attention due to their provable security guarantees, theoretically safeguarding the privacy of sensitive data and LLMs weights. These cryptographic techniques have rapidly permeated key stages of LLMs, including data selection, fine-tuning, and inference. Despite these advancements, there is currently no comprehensive survey summarizing the work related to cryptography-based privacy-preserving LLMs (CPLMs), leaving their research isolated and fragmented. To fill this gap, We provide a comprehensive review of existing CPLMs research and systematically classifies them, enabling researchers to effectively coordinate optimization strategies for the efficient design of CPLMs algorithms. Finally, based on the limitations of current CPLMs research, we outline several promising directions for future exploration.