  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.
