Privacy-Preserving Prompt Tuning for Large Language Model Services. (arXiv:2305.06212v1 [cs.CL])

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 …

customization data emerging language language models large large language model llm llms nature preservation privacy private private data scenario service services