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剥削隐私保护在线大型语言模型使用的快速技术

Youxiang Zhu¹, Ning Gao¹, Xiaohui Liang¹

¹Department of Computer Science, University of Massachusetts Boston, MA, USA.

... IEEE Global Communications Conference. IEEE Global Communications Conference

|September 18, 2025

概括

一个新的本地隐私保护提示助手 (LPPA) 帮助用户在在线大型语言模型 (LLM) 提示中保护敏感信息. LPPA修改提示以保护隐私,同时保持LLM输出实用程序.

科学领域:

计算机科学计算机科学
人工智能的人工智能
网络安全网络安全

背景情况:

在线大型语言模型 (LLM) 越来越多地用于财务咨询等敏感任务.
直接向LLM服务器快速提交可能会暴露私人数据并使用户配置文件.
现有的方法缺乏有效的用户控制的隐私保护机制,用于LLM交互.

研究的目的:

引入一个本地隐私保护快速助手 (LPPA),以平衡快速隐私和LLM输出实用程序.
开发用于识别和缓解用户提示符中的敏感关键字的方法.
让用户能够保护敏感信息,而不会影响LLM产生的内容的有用性.

主要方法:

提出了一个隐私模块来检测提示符中的敏感关键字.
实施了四种隐私技术:删除,掩盖,替换和重写以获得关键字保护.
开发了一种实用推断模型,以预测即时修改对本地LLM输出质量的影响.
通过使用真实世界的用户提示来评估LPPA系统.

主要成果:

"删除"技术在保护隐私方面表现出最高的性能.
该LPPA有效地识别敏感的关键词,并建议立即修改.
实用性推断模型准确地预测了快速变化的对LLM输出的影响.
用户可以调整提示以提高隐私,同时保持令人满意的LLM实用程序.

结论:

当地隐私保护快速助理 (LPPA) 提供了一种实用的解决方案,可以通过在线LLMs来增强用户隐私.
快速修改技术,特别是删除,在保护敏感数据方面是有效的.
LPPA使用户能够控制他们的数据隐私,而不会显著地损失LLM的功能.

关键词:

保护隐私的技术保护隐私的技术大型语言模型.在线和离线在线和离线.

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