As Large Language Models (LLMs) become integral to scientific workflows, concerns over the confidentiality and ethical handling of confidential data have emerged. This paper explores data exposure risks through LLM-powered scientific tools, which can inadvertently leak confidential information, including intellectual property and proprietary data, from scientists' perspectives. We propose "DataShield", a framework designed to detect confidential data leaks, summarize privacy policies, and visualize data flow, ensuring alignment with organizational policies and procedures. Our approach aims to inform scientists about data handling practices, enabling them to make informed decisions and protect sensitive information. Ongoing user studies with scientists are underway to evaluate the framework's usability, trustworthiness, and effectiveness in tackling real-world privacy challenges.

本研究针对大语言模型在科学工作流程中引发的机密数据处理和泄露风险问题，提出了一种名为“DataShield”的框架。该框架旨在检测机密数据泄露、汇总隐私政策并可视化数据流，从而帮助科学家合理处理敏感信息，做出明智的决策，以应对现实世界中的隐私挑战。

隐私与可解释性相结合：管理机密数据与大语言模型驱动科学中的透明政策