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Computer Science > Computation and Language

arXiv:2410.03925 (cs)
[Submitted on 4 Oct 2024]

Title:C3PA: An Open Dataset of Expert-Annotated and Regulation-Aware Privacy Policies to Enable Scalable Regulatory Compliance Audits

Authors:Maaz Bin Musa, Steven M. Winston, Garrison Allen, Jacob Schiller, Kevin Moore, Sean Quick, Johnathan Melvin, Padmini Srinivasan, Mihailis E. Diamantis, Rishab Nithyanand
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Abstract:The development of tools and techniques to analyze and extract organizations data habits from privacy policies are critical for scalable regulatory compliance audits. Unfortunately, these tools are becoming increasingly limited in their ability to identify compliance issues and fixes. After all, most were developed using regulation-agnostic datasets of annotated privacy policies obtained from a time before the introduction of landmark privacy regulations such as EUs GDPR and Californias CCPA. In this paper, we describe the first open regulation-aware dataset of expert-annotated privacy policies, C3PA (CCPA Privacy Policy Provision Annotations), aimed to address this challenge. C3PA contains over 48K expert-labeled privacy policy text segments associated with responses to CCPA-specific disclosure mandates from 411 unique organizations. We demonstrate that the C3PA dataset is uniquely suited for aiding automated audits of compliance with CCPA-related disclosure mandates.
Comments: 9 pages, EMNLP 2024
Subjects: Computation and Language (cs.CL); Information Retrieval (cs.IR)
Cite as: arXiv:2410.03925 [cs.CL]
  (or arXiv:2410.03925v1 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2410.03925

Submission history

From: Maaz Bin Musa [view email]
[v1] Fri, 4 Oct 2024 21:04:39 UTC (1,095 KB)
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