2023/11/07 by Fei Zheng, Zheng, Fei · 3 citations
Computer Science · #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Privacy-Preserving Technologies in Data
paper · pdf · doi:10.48550/arxiv.2311.07585
openalex publication_date 2023/11/07 · openalex created_date 2023/11/16 · openalex updated_date 2026/07/28
Recently, large language models (LLMs) have drawn extensive attention from academia and the public, due to the advent of the ChatGPT. While LLMs show their astonishing ability in text generation for various tasks, privacy concerns limit their usage in real-life businesses. More specifically, either the user's inputs (the user sends the query to the model-hosting server) or the model (the user downloads the complete model) itself will be revealed during the usage. Vertical federated learning (VFL) is a promising solution to this kind of problem. It protects both the user's input and the knowledge of the model by splitting the model into a bottom part and a top part, which is maintained by the user and the model provider, respectively. However, in this paper, we demonstrate that in LLMs, VFL fails to protect the user input since it is simple and cheap to reconstruct the input from the intermediate embeddings. Experiments show that even with a commercial GPU, the input sentence can be reconstructed in only one second. We also discuss several possible solutions to enhance the privacy of vertical federated LLMs.