Automated Detection and Analysis of Data Practices Using A Real-World Corpus

Anonymous

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.

Paper Type: short

Research Area: NLP Applications

Contribution Types: Approaches to low-resource settings, Data resources

Languages Studied: English

Preprint Status: We plan to release a non-anonymous preprint in the next two months (i.e., during the reviewing process).

A1: yes

A1 Elaboration For Yes Or No: 6

A2: yes

A2 Elaboration For Yes Or No: 6

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A3 Elaboration For Yes Or No: 1

B: yes

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B1 Elaboration For Yes Or No: 3.1

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B2 Elaboration For Yes Or No: 3.1

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B3 Elaboration For Yes Or No: 3.1

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B5 Elaboration For Yes Or No: 3.1

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B6 Elaboration For Yes Or No: 3.1

C: yes

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C1 Elaboration For Yes Or No: 3.2

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C2 Elaboration For Yes Or No: 3.2

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D: no

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E: yes

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E1 Elaboration For Yes Or No: Appendix