{A New Hope}: Contextual Privacy Policies for Mobile Applications and An Approach Toward Automated Generation

• URL: http://arxiv.org/abs/2402.14544v2
• Date: Sun, 10 Mar 2024 06:19:10 GMT
• Title: {A New Hope}: Contextual Privacy Policies for Mobile Applications and An Approach Toward Automated Generation
• Authors: Shidong Pan, Zhen Tao, Thong Hoang, Dawen Zhang, Tianshi Li, Zhenchang Xing, Sherry Xu, Mark Staples, Thierry Rakotoarivelo, David Lo
• Abstract summary: The aim of contextual privacy policies ( 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. A human evaluation shows that 77% of the extracted privacy policy segments were perceived as well-aligned with the detected contexts.
• Score: 19.578130824867596
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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/

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