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Prompt tuning provides an efficient way for users to customize Large LanguageModels (LLMs) with their private data in the emerging LLM service scenario.However, the sensitive nature of private data brings the need for privacypreservation in LLM service customization. Based on prompt tuning, we proposePrivacy-Preserving Prompt Tuning (RAPT), a framework that provides privacyguarantees 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, weintroduce a novel privatized token reconstruction task that is trained jointlywith the downstream task, allowing LLMs to learn better task-dependentrepresentations. Despite the simplicity of our framework, experiments show thatRAPT achieves competitive performance across tasks while providing privacyguarantees against adversaries.

Privacy-Preserving Prompt Tuning for Large Language Model Services