PrivCAPTCHA: Interactive CAPTCHA to Facilitate Effective Comprehension of APP Privacy Policy

要旨

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.

著者
Shuning Zhang
Tsinghua University, Beijing, China
Xin Yi
Tsinghua University, Beijing, China
Shixuan Li
Tsinghua University, Beijing, China
Haobin Xing
Tsinghua University, Beijing, China
Hewu Li
Tsinghua University, Beijing, China
DOI

10.1145/3706598.3713928

論文URL

https://dl.acm.org/doi/10.1145/3706598.3713928

動画

会議: CHI 2025

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)

セッション: Engaging Users for Security and Privacy

G418+G419
7 件の発表
2025-05-01 01:20:00
2025-05-01 02:50:00
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