Automated Detection and Analysis of Data Practices Using A Real-World Corpus
Mukund Srinath, Pranav Narayanan Venkit, Maria Badillo, Florian Schaub, C. Giles, Shomir Wilson
Abstract
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.- Anthology ID:
- 2024.findings-acl.271
- Volume:
- Findings of the Association for Computational Linguistics: ACL 2024
- Month:
- August
- Year:
- 2024
- Address:
- Bangkok, Thailand
- Editors:
- Lun-Wei Ku, Andre Martins, Vivek Srikumar
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 4567–4574
- Language:
- URL:
- https://aclanthology.org/2024.findings-acl.271
- DOI:
- 10.18653/v1/2024.findings-acl.271
- Cite (ACL):
- Mukund Srinath, Pranav Narayanan Venkit, Maria Badillo, Florian Schaub, C. Giles, and Shomir Wilson. 2024. Automated Detection and Analysis of Data Practices Using A Real-World Corpus. In Findings of the Association for Computational Linguistics: ACL 2024, pages 4567–4574, Bangkok, Thailand. Association for Computational Linguistics.
- Cite (Informal):
- Automated Detection and Analysis of Data Practices Using A Real-World Corpus (Srinath et al., Findings 2024)
- PDF:
- https://preview.aclanthology.org/dois-2013-emnlp/2024.findings-acl.271.pdf