Privacy Meets Explainability: Managing Confidential Data and Transparency Policies in LLM-Empowered Science

2025-04-14

AI Summary

To address privacy and transparency risks—particularly unintended leakage of confidential data (e.g., intellectual property, proprietary datasets)—in LLM-driven scientific tools, this paper introduces DataShield: the first unified framework integrating privacy leak detection, interpretable privacy policy analysis, and interactive data lineage visualization. Methodologically, it combines rule-based engines, a lightweight NER model, policy text summarization, and dynamic lineage graph rendering. Evaluated on real-world scientific toolchains, DataShield achieves 92% accuracy in identifying sensitive data leaks. A user study with domain scientists shows that 87% report significantly improved awareness of privacy risks and greater confidence in data-handling decisions. This work is the first to jointly enforce policy alignment, scientist-centered decision support, and regulatory compliance within LLM-based research infrastructure—establishing a practical, deployable pathway toward trustworthy scientific AI.

Technology Category

• Machine Learning: Privacy
• Natural Language Processing: Safety and Robustness
• Philosophy and Ethics of AI: Privacy & Security

Application Category

• Security and Privacy: Data transparency and provenance
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

Abstract

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.

Problem

• Detect confidential data leaks in LLM-powered scientific tools
• Summarize privacy policies for ethical data handling
• Visualize data flow to align with organizational policies

Innovation

• Detects confidential data leaks automatically
• Summarizes privacy policies efficiently
• Visualizes data flow for transparency