Automated Detection and Analysis of Data Practices Using A Real-World
  Corpus
Mukund Srinath Pranav Venkit Maria Badillo Florian Schaub C. Lee Giles Shomir Wilson
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.