LegalGuardian：法律实践中大型语言模型安全集成的隐私保护框架
LegalGuardian: A Privacy-Preserving Framework for Secure Integration of Large Language Models in Legal Practice
AI总结：
LegalGuardian提出轻量级隐私保护框架，通过NER和本地LLM屏蔽PII，实现高F1分数，使律师安全使用LLM工具而不泄露客户信息。
摘要：
大型语言模型（LLMs）有望通过自动化复杂任务和改善司法可及性来推进法律实践。然而，其采用受到客户保密性问题的限制，尤其是当律师在提示中包含敏感的个人身份信息（PII）时，存在未经授权数据暴露的风险。为缓解这一问题，我们引入了LegalGuardian，一个专为使用基于LLM工具的律师设计的轻量级隐私保护框架。LegalGuardian采用命名实体识别（NER）技术和本地LLMs来屏蔽和还原提示中的机密PII，在任何外部交互之前保护敏感数据。我们详细介绍了其开发过程，并使用移民法场景中的合成提示库评估其有效性。通过比较传统NER模型与一次性提示的本地LLM，我们发现LegalGuardian在使用GLiNER时PII检测的F1分数达到93%，使用Qwen2.5-14B时达到97%。语义相似性分析确认该框架在输出中保持高保真度，确保基于LLM工具的稳健实用性。我们的研究结果表明，法律专业人士可以在不损害客户保密性或法律文件质量的情况下利用先进AI技术。
英文摘要：
Large Language Models (LLMs) hold promise for advancing legal practice by automating complex tasks and improving access to justice. However, their adoption is limited by concerns over client confidentiality, especially when lawyers include sensitive Personally Identifiable Information (PII) in prompts, risking unauthorized data exposure. To mitigate this, we introduce LegalGuardian, a lightweight, privacy-preserving framework tailored for lawyers using LLM-based tools. LegalGuardian employs Named Entity Recognition (NER) techniques and local LLMs to mask and unmask confidential PII within prompts, safeguarding sensitive data before any external interaction. We detail its development and assess its effectiveness using a synthetic prompt library in immigration law scenarios. Comparing traditional NER models with one-shot prompted local LLM, we find that LegalGuardian achieves a F1-score of 93% with GLiNER and 97% with Qwen2.5-14B in PII detection. Semantic similarity analysis confirms that the framework maintains high fidelity in outputs, ensuring robust utility of LLM-based tools. Our findings indicate that legal professionals can harness advanced AI technologies without compromising client confidentiality or the quality of legal documents.
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。
