InferDPT: Privacy-Preserving Inference for Closed-Box Large Language Models
Meng Tong, Kejiang Chen, Jie Zhang, Yuang Qi, Weiming Zhang, Nenghai Yu, Tianwei Zhang, Zhikun Zhang · IEEE Transactions on Dependable and Secure Computing · 2025
Large language models(LLMs), represented by ChatGPT, have greatly simplified text generation tasks. However, they have also raised concerns about privacy risks such as data leakage and unauthorized information collection. Existing solutions for privacy-preserving inference face practical challenges related to computational time and communication costs. In this article, we proposeInferDPT, the first practical framework for privacy-preservingInference of closed-box LLMs, implementingDifferentialPrivacy inText generation.InferDPTcomprises two key modules: the “perturbation module” utilizes the differentially private mechanism to generate a perturbed prompt, facilitating privacy-preserving inference with closed-box LLMs; the “extraction module”, inspired by knowledge distillation and phenomenon we observed, extracts coherent and consistent text from the perturbed generation result, ensuring successful text generation completion. To achieve a better balance between utility and privacy protection, we introduce RANTEXT, a novel differentially private mechanism integrated into the perturbation module ofInferDPT, which introduces the concept of “RANdom adjacency list” forTEXTperturbation within the prompt. Experimental results across three datasets demonstrate that the text generation quality ofInferDPTis comparable to that of non-private GPT-4, and RANTEXT surpasses existing state-of-the-art mechanisms, namely, SANTEXT+ and CUSTEXT+ in the trade-off between privacy and utility. Even with a privacy parameter$\varepsilon$value of 6.0, RANTEXT achieves an average privacy protection level of exceeding 0.90 against the embedding inversion attacks, which is 0.58× higher than that of SANTEXT+ and 3.35× higher than that of CUSTEXT+.