发表机构
ETH Zurich; Carnegie Mellon University（苏黎世联邦理工学院; 卡内基梅隆大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究多语言环境下隐私政策审计，利用大语言模型构建跨语言分类器，通过对西班牙谷歌应用商店应用的大规模审计，发现公共部门与商业应用语言使用差异及声明与实际做法的差异，揭示仅英语审计掩盖透明度差距的问题。
AI 中文摘要
自动化隐私政策分析有助于大规模评估数字生态系统的透明度，但现有审计管道主要以英语为中心，限制了对多语言环境的评估。本文研究大语言模型（LLMs）能否在无需特定语言适应的情况下扩展英语以外的隐私政策分析。通过组装涵盖欧盟24种官方语言的评估语料库，基于LLM的分类器实现稳定跨语言性能。在对西班牙谷歌应用商店2611个安卓应用的大规模审计中，发现公共部门应用和商业应用语言使用差异及声明与实际做法的系统差异。结果表明仅英语的隐私审计会掩盖多语言环境中的透明度差距。
英文摘要
Automated analyses of privacy policies enable large-scale assessments of transparency in digital ecosystems, yet existing auditing pipelines remain predominantly English-centric. This limits their ability to systematically evaluate multilingual environments, as in the European Union, where many services disclose privacy practices only in local languages. This paper examines whether large language models (LLMs) can extend privacy policy analysis beyond English without requiring language-specific adaptation, thus empowering large-scale auditing in linguistically diverse app ecosystems. We assemble an evaluation corpus spanning all 24 official EU languages from translated versions of two established expert-annotated datasets (OPP-115 and MAPP) and assess translation fidelity through automated metrics and targeted legal-expert review. Our LLM-based classifier for identifying categories of personal data collection achieves stable cross-lingual performance, with macro-F1 scores ranging between 0.91 and 0.94. We then leverage this capability in a large-scale audit of 2,611 Android applications from the Spanish Google Play Store. Combining multilingual privacy policy analysis with the evaluation of corresponding privacy labels and runtime network traffic exposes an important linguistic barrier: public-sector apps predominantly provide privacy policies in Spanish, whereas popular commercial apps mostly provide them in English. We reveal systematic discrepancies between declared and observed practices, especially in public-sector apps. Overall, our results indicate how English-only privacy audits can systematically obfuscate transparency gaps in multilingual environments.
