用本地小模型过滤隐私信息，保护用户对话不被云端大模型泄露
Guarding Your Conversations: Privacy Gatekeepers for Secure Interactions with Cloud-Based AI Models
- 本地部署轻量模型作为隐私守门员，拦截敏感内容
- 实验证明隐私提升显著，响应质量几乎不受影响
- 适合对数据安全要求高的个人或企业用户
大型语言模型（LLM）的交互特性导致用户前所未有地共享个人和隐私信息。即使用户选择禁止其数据用于训练，当模型服务商位于隐私法律薄弱、政府监控严格或数据安全差的司法管辖区时，这些设置仍无法提供充分保护，敏感信息（包括个人身份信息PII）泄露风险依然很高。为此，我们提出“LLM守门员”概念：一个轻量级、本地运行的模型，在用户查询发送至可能不可信但功能强大的云端大模型前，先过滤掉敏感信息。通过人类受试者实验，我们证明该双模型方案引入的开销极小，显著提升用户隐私保护，且不影响大模型响应质量。
原文摘要 · Abstract (English)
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.
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