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

• URL: http://arxiv.org/abs/2502.15233v1
• Date: Fri, 21 Feb 2025 06:15:53 GMT
• Title: A General Pseudonymization Framework for Cloud-Based LLMs: Replacing Privacy Information in Controlled Text Generation
• Authors: Shilong Hou, Ruilin Shang, Zi Long, Xianghua Fu, Yin Chen,
• Abstract summary: ChatGPT services leverage cloud-based large language models (LLMs)<n>Privacy concerns arise as prompts are transmitted and processed by the model providers.<n>We propose a general pseudonymization framework applicable to cloud-based LLMs.
• Score: 0.6699777383856287
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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