Privacy policies have emerged as the predominant approach to conveying privacy notices to mobile application users. In an effort to enhance both readability and user engagement, the concept of contextual privacy policies (CPPs) has been proposed by researchers. The aim of CPPs is to fragment privacy policies into concise snippets, displaying them only within the corresponding contexts within the application's graphical user interfaces (GUIs). In this paper, we first formulate CPP in mobile application scenario, and then present a novel multimodal framework, named SeePrivacy, specifically designed to automatically generate CPPs for mobile applications. This method uniquely integrates vision-based GUI understanding with privacy policy analysis, achieving 0.88 precision and 0.90 recall to detect contexts, as well as 0.98 precision and 0.96 recall in extracting corresponding policy segments. A human evaluation shows that 77% of the extracted privacy policy segments were perceived as well-aligned with the detected contexts. These findings suggest that SeePrivacy could serve as a significant tool for bolstering user interaction with, and understanding of, privacy policies. Furthermore, our solution has the potential to make privacy notices more accessible and inclusive, thus appealing to a broader demographic. A demonstration of our work can be accessed at https://cpp4app.github.io/SeePrivacy/

翻译：隐私政策已成为向移动应用用户传达隐私通知的主要方式。为提高可读性和用户参与度，研究者提出了上下文隐私政策（CPPs）的概念，其目标是将隐私政策拆分为简洁的片段，仅在应用图形用户界面（GUIs）的相应上下文中显示。本文首先定义了移动应用场景下的CPP，随后提出了一种名为SeePrivacy的新型多模态框架，专门用于自动生成移动应用的CPP。该方法创新性地将基于视觉的GUI理解与隐私政策分析相结合，在上下文检测中实现了0.88的精确率和0.90的召回率，在提取相应政策片段中达到了0.98的精确率和0.96的召回率。人工评估表明，77%的提取隐私政策片段被认为与检测到的上下文高度匹配。这些发现表明，SeePrivacy可作为增强用户与隐私政策互动及理解的重要工具。此外，我们的解决方案有望使隐私通知更易获取且更具包容性，从而吸引更广泛的用户群体。相关演示可访问 https://cpp4app.github.io/SeePrivacy/ 查看。