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.

翻译：隐私政策对于告知用户数据实践至关重要，但其冗长与复杂性常常使用户望而却步。本文提出一种自动化方法，用于识别隐私政策中不同粒度层级的数据实践并对其进行可视化。利用ToS;DR平台的众包标注，我们实验了多种方法，将政策片段与预定义的数据实践描述进行匹配。进一步通过案例研究，在真实政策文本上评估了该方法，证实其能有效简化复杂政策。实验结果表明，我们的方法能够准确地将数据实践描述与政策片段相匹配，从而便于向用户呈现简化后的隐私信息。