  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/
