Privacy policies are crucial for informing users about data practices, yet
their length and complexity often deter users from reading them. In this paper,
we propose an automated approach to identify and visualize data practices
within privacy policies at different levels of detail. Leveraging crowd-sourced
annotations from the ToS;DR platform, we experiment with various methods to
match policy excerpts with predefined data practice descriptions. We further
conduct a case study to evaluate our approach on a real-world policy,
demonstrating its effectiveness in simplifying complex policies. Experiments
show that our approach accurately matches data practice descriptions with
policy excerpts, facilitating the presentation of simplified privacy
information to users.

我们提出了一种自动化的方法来识别和可视化不同细节级别的隐私政策中的数据实践，通过利用 ToS;DR 平台的众包注释，我们尝试使用各种方法将政策摘录与预定义的数据实践描述相匹配，进一步进行了一个案例研究来评估我们的方法在真实政策上的有效性，实验表明我们的方法准确匹配了数据实践描述和政策摘录，为用户呈现了简化的隐私信息。