用图分析技术拆解隐私政策，帮用户看懂数据如何被收集和使用。
Unveiling Privacy Policy Complexity: An Exploratory Study Using Graph Mining, Machine Learning, and Natural Language Processing
- 将隐私条款转为图模型，用可视化展示数据处理关系。
- 通过图聚类发现用户行为追踪和数据共享模式，识别风险点。
- 适合关注隐私安全的普通用户、监管机构和数据审计人员。
隐私政策通常冗长复杂，非专业人士难以理解，导致个人数据的收集、处理与分享缺乏透明度。随着在线隐私问题日益突出，开发自动化分析工具以识别潜在风险至关重要。本研究探索交互式图可视化在提升用户对隐私政策理解方面的潜力，将政策条款转化为结构化图模型，使复杂关系更易理解，帮助用户做出知情决策（研究问题1）。同时，采用图挖掘算法结合t-SNE和PCA等降维技术，识别出如用户活动、设备信息等关键主题，并评估聚类效果。结果表明，基于图的聚类显著提升了政策内容的可解释性，揭示了用户追踪与数据共享的模式，支持执法调查并识别合规漏洞。该研究通过融合交互可视化与图挖掘，推进了AI驱动的隐私政策审计工具发展，增强透明度与问责性。
原文摘要 · Abstract (English)
Privacy policy documents are often lengthy, complex, and difficult for non-expert users to interpret, leading to a lack of transparency regarding the collection, processing, and sharing of personal data. As concerns over online privacy grow, it is essential to develop automated tools capable of analyzing privacy policies and identifying potential risks. In this study, we explore the potential of interactive graph visualizations to enhance user understanding of privacy policies by representing policy terms as structured graph models. This approach makes complex relationships more accessible and enables users to make informed decisions about their personal data (RQ1). We also employ graph mining algorithms to identify key themes, such as User Activity and Device Information, using dimensionality reduction techniques like t-SNE and PCA to assess clustering effectiveness. Our findings reveal that graph-based clustering improves policy content interpretability. It highlights patterns in user tracking and data sharing, which supports forensic investigations and identifies regulatory non-compliance. This research advances AI-driven tools for auditing privacy policies by integrating interactive visualizations with graph mining. Enhanced transparency fosters accountability and trust.
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