守护你的对话：用于与基于云的AI模型安全交互的隐私守门人
Guarding Your Conversations: Privacy Gatekeepers for Secure Interactions with Cloud-Based AI Models
AI总结：
本文提出轻量级本地运行的“LLM守门人”模型，在用户查询发送至云端LLM前过滤敏感信息，实验证明该双模型方法能以极小开销显著增强隐私且不降低响应质量。
摘要：
大型语言模型（LLMs）的交互特性使其紧密追踪用户数据和上下文，这促使用户以前所未有的方式分享个人和私人信息。即使用户选择不允许其数据被用于训练，当LLM提供商在隐私法律薄弱、政府监控具有侵入性或数据安全实践较差的司法管辖区运营时，这些隐私设置提供的保护也有限。在这种情况下，敏感信息（包括个人身份信息（PII））被不当处理或泄露的风险仍然很高。为了解决这一问题，我们提出了“LLM守门人”的概念，这是一个轻量级、本地运行的模型，在用户查询被发送到可能不可信但能力强大的基于云的LLM之前，过滤掉其中的敏感信息。通过人类受试者实验，我们证明这种双模型方法引入的开销极小，同时显著增强了用户隐私，且不损害LLM响应的质量。
英文摘要：
The interactive nature of Large Language Models (LLMs), which closely track user data and context, has prompted users to share personal and private information in unprecedented ways. Even when users opt out of allowing their data to be used for training, these privacy settings offer limited protection when LLM providers operate in jurisdictions with weak privacy laws, invasive government surveillance, or poor data security practices. In such cases, the risk of sensitive information, including Personally Identifiable Information (PII), being mishandled or exposed remains high. To address this, we propose the concept of an "LLM gatekeeper", a lightweight, locally run model that filters out sensitive information from user queries before they are sent to the potentially untrustworthy, though highly capable, cloud-based LLM. Through experiments with human subjects, we demonstrate that this dual-model approach introduces minimal overhead while significantly enhancing user privacy, without compromising the quality of LLM responses.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
