Privacy policies have become the most critical approach to safeguarding individuals' privacy and digital security. To enhance their presentation and readability, researchers propose the concept of contextual privacy policies (CPPs), aiming to fragment policies into shorter snippets and display them only in corresponding contexts. In this paper, we propose a novel multi-modal framework, namely SeePrivacy, designed to automatically generate contextual privacy policies for mobile apps. Our method synergistically combines mobile GUI understanding and privacy policy document analysis, yielding an impressive overall 83.6% coverage rate for privacy-related context detection and an accuracy of 0.92 in extracting corresponding policy segments. Remarkably, 96% of the retrieved policy segments can be correctly matched with their contexts. The user study shows SeePrivacy demonstrates excellent functionality and usability (4.5/5). Specifically, participants exhibit a greater willingness to read CPPs (4.1/5) compared to original privacy policies (2/5). Our solution effectively assists users in comprehending privacy notices, and this research establishes a solid foundation for further advancements and exploration.

翻译：隐私政策已成为保护个人隐私与数字安全的最关键手段。为提升呈现方式与可读性，研究者提出上下文隐私政策（CPP）概念，旨在将政策拆分为简短片段，并仅在对应场景中展示。本文提出一种名为SeePrivacy的新型多模态框架，可自动为移动应用生成上下文隐私政策。该方法协同融合移动图形用户界面理解与隐私政策文档分析，在隐私相关上下文检测中实现83.6%的总体覆盖率，在提取对应政策片段时达到0.92的准确率。值得注意的是，96%的检索政策片段可与其上下文正确匹配。用户研究表明，SeePrivacy展现出卓越的功能性与可用性（4.5/5分）。具体而言，相较于原始隐私政策（2/5分），参与者表现出更高的CPP阅读意愿（4.1/5分）。本方案有效协助用户理解隐私通知，且该研究为未来进一步发展与探索奠定了坚实基础。