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最关键的手段。为提升其呈现方式与可读性，研究者提出上下文隐私政策（Contextual Privacy Policies, CPPs）概念，旨在将完整政策拆分为简短片段，并仅在对应场景中展示。本文提出一种新颖的多模态框架SeePrivacy，用于自动生成移动应用的上下文隐私政策。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，在隐私相关场景检测中取得了83.6%的总体覆盖率，相应政策片段提取准确率达0.92。值得注意的是，96%的检索政策片段能够与其对应场景正确匹配。用户研究表明，SeePrivacy展现了卓越的功能性与可用性（4.5/5分）。具体而言，参与者对CPPs的阅读意愿（4.1/5分）显著高于原始隐私政策（2/5分）。该方案有效帮助用户理解隐私通知，本研究为后续深入探索奠定了坚实基础。