A Fine-grained Chinese Software Privacy Policy Dataset for Sequence Labeling and Regulation Compliant Identification

TL;DR

This paper introduces CA4P-483, the first Chinese privacy policy dataset with detailed annotations, enabling NLP analysis for understanding and regulating software privacy policies in Chinese applications.

Contribution

It creates a comprehensive Chinese privacy policy dataset with fine-grained annotations, addressing language and legal requirement gaps in existing datasets.

Findings

Baseline models achieve moderate performance on the dataset.

Analysis reveals challenges in sequence labeling of legal and technical terms.

Potential applications include regulation compliance and program analysis.

Abstract

Privacy protection raises great attention on both legal levels and user awareness. To protect user privacy, countries enact laws and regulations requiring software privacy policies to regulate their behavior. However, privacy policies are written in natural languages with many legal terms and software jargon that prevent users from understanding and even reading them. It is desirable to use NLP techniques to analyze privacy policies for helping users understand them. Furthermore, existing datasets ignore law requirements and are limited to English. In this paper, we construct the first Chinese privacy policy dataset, namely CA4P-483, to facilitate the sequence labeling tasks and regulation compliance identification between privacy policies and software. Our dataset includes 483 Chinese Android application privacy policies, over 11K sentences, and 52K fine-grained annotations. We evaluate families of robust and representative baseline models on our dataset. Based on baseline performance, we provide findings and potential research directions on our dataset. Finally, we investigate the potential applications of CA4P-483 combing regulation requirements and program analysis.