Traditional app privacy policies are often lengthy and non-interactive, leading users to skip them and remain uninformed. To address this, we proposed PrivCAP, a technique to enhance user comprehension by presenting policies in a concise, interactive format. PrivCAP adopted a CAPTCHA-based design, requiring users to interact with clickable chunks of concise policy content, thus reducing physical and cognitive load. A formative study (N=38) demonstrated that participants valued informed consent alongside concerns over data collection and sharing, marking the first such evaluation among Chinese users. This study further found a preference for concise visualizations and interactable formats. PrivCAP, leveraging few-shot prompting on Large Language Models (LLMs), accurately translates privacy policies into clickable, chunked formats optimized for smartphone screens. In an evaluation (N=28), PrivCAP outperformed traditional policy presentations in improving user understanding, reducing cognitive load, and maintaining efficiency, with participants favoring its engaging design and reporting more informed decision-making.
https://dl.acm.org/doi/10.1145/3706598.3713928
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