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

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

Maaz Bin Musa, Steven M Winston, Garrison Allen, Jacob Schiller, Kevin Moore, Sean Quick, Johnathan Melvin, Padmini Srinivasan, Mihailis E Diamantis and Rishab Nithyanand
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pp.3710-3722
11/2024
DOI: 10.18653/v1/2024.emnlp-main.217
url
https://doi.org/10.18653/v1/2024.emnlp-main.217View
Published (Version of record) Open Access

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 EU’s GDPR and California’s 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.
 Computer Science - Computation and Language Computer Science - Information Retrieval

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