Prompt tuning provides an efficient way for users to customize Large Language Models (LLMs) with their private data in the emerging LLM service scenario. However, the sensitive nature of private data brings the need for privacy preservation in LLM service customization. Based on prompt tuning, we propose Privacy-Preserving Prompt Tuning (RAPT), a framework that provides privacy guarantees for LLM services. \textsc{rapt} adopts a local privacy setting, allowing users to privatize their data locally with local differential privacy. As prompt tuning performs poorly when directly trained on privatized data, we introduce a novel privatized token reconstruction task that is trained jointly with the downstream task, allowing LLMs to learn better task-dependent representations. Despite the simplicity of our framework, experiments show that RAPT achieves competitive performance across tasks while providing privacy guarantees against adversaries.

本文提出了基于响应式调整的隐私保护响应式调整（RAPT）框架，结合本地差分隐私的局部隐私设置，通过 token 重建任务与下游任务一起进行训练，旨在提供针对大型语言模型的隐私保障。实验表明，RAPT 在各种任务上均表现出优秀的性能并针对窃密者提供隐私保障。

大型语言模型服务的隐私保护提示调整