A General Pseudonymization Framework for Cloud-Based LLMs: Replacing Privacy Information in Controlled Text Generation

TL;DR

This paper introduces a comprehensive pseudonymization framework designed to protect user privacy during interactions with cloud-based large language models, balancing privacy and utility effectively.

Contribution

It presents the first detailed definition of a pseudonymization framework tailored for cloud-based LLMs, addressing privacy risks during inference.

Findings

Framework effectively balances privacy and utility

Experimental results validate privacy protection effectiveness

Code is publicly available for implementation

Abstract

An increasing number of companies have begun providing services that leverage cloud-based large language models (LLMs), such as ChatGPT. However, this development raises substantial privacy concerns, as users' prompts are transmitted to and processed by the model providers. Among the various privacy protection methods for LLMs, those implemented during the pre-training and fine-tuning phrases fail to mitigate the privacy risks associated with the remote use of cloud-based LLMs by users. On the other hand, methods applied during the inference phrase are primarily effective in scenarios where the LLM's inference does not rely on privacy-sensitive information. In this paper, we outline the process of remote user interaction with LLMs and, for the first time, propose a detailed definition of a general pseudonymization framework applicable to cloud-based LLMs. The experimental results demonstrate that the proposed framework strikes an optimal balance between privacy protection and utility. The code for our method is available to the public at https://github.com/Mebymeby/Pseudonymization-Framework.