为云端大模型设计通用匿名化框架，保护用户隐私同时保持模型可用性。
A General Pseudonymization Framework for Cloud-Based LLMs: Replacing Privacy Information in Controlled Text Generation
- 提出适用于云端大模型的通用匿名化框架，处理用户输入中的敏感信息。
- 实验表明该框架在隐私保护与模型效果间取得良好平衡。
- 适合关注云服务中数据隐私问题的研究者与开发者使用。
越来越多企业开始提供基于云端大语言模型（LLMs）的服务，如ChatGPT。然而，这引发重大隐私担忧：用户的提示词会被传输并由模型提供商处理。现有隐私保护方法多集中在预训练和微调阶段，无法应对用户远程使用时的隐私风险；而推理阶段的方法仅适用于不依赖敏感信息的场景。本文首次系统分析了用户与云端LLM的远程交互流程，并提出一种通用的匿名化框架。实验结果表明，该框架在隐私保护与模型效用之间实现良好权衡。代码已公开于 https://github.com/Mebymeby/Pseudonymization-Framework。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。