# Enabling Multilingual Privacy Policy Audits: Large-Scale Analysis of Spanish Mobile Apps

arXiv: https://arxiv.org/abs/2607.18424 · HTML: https://mlsanity.com/paper/2607.18424/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 75 references that are in the map.
- First posted on arXiv 20 Jul 2026.

## Builds on (most cited first, 15 of 15)

- [COMET: A Neural Framework for MT Evaluation](https://mlsanity.com/paper/2009.09025/) (2020)
- [CLAUDETTE: an Automated Detector of Potentially Unfair Clauses in Online Terms of Service](https://mlsanity.com/paper/1805.01217/) (2018)
- [Privacy Policies over Time: Curation and Analysis of a Million-Document Dataset](https://mlsanity.com/paper/2008.09159/) (2020)
- [Privacy at Scale: Introducing the PrivaSeer Corpus of Web Privacy Policies](https://mlsanity.com/paper/2004.11131/) (2020)
- [Large Language Models: A New Approach for Privacy Policy Analysis at Scale](https://mlsanity.com/paper/2405.20900/) (2024)
- [Automatic Detection of Vague Words and Sentences in Privacy Policies](https://mlsanity.com/paper/1808.06219/) (2018)
- [ReCon: Revealing and Controlling PII Leaks in Mobile Network Traffic](https://mlsanity.com/paper/1507.00255/) (2015)
- [Are iPhones Really Better for Privacy? Comparative Study of iOS and Android Apps](https://mlsanity.com/paper/2109.13722/) (2021)
- [Unpacking Privacy Labels: A Measurement and Developer Perspective on Google's Data Safety Section](https://mlsanity.com/paper/2306.08111/) (2023)
- [Third Party Tracking in the Mobile Ecosystem](https://mlsanity.com/paper/1804.03603/) (2018)
- [ATLAS: Automatically Detecting Discrepancies Between Privacy Policies and Privacy Labels](https://mlsanity.com/paper/2306.09247/) (2023)
- [Evaluating Privacy Perceptions, Experience, and Behavior of Software Development Teams](https://mlsanity.com/paper/2404.01283/) (2024)
- [Creation and Analysis of an International Corpus of Privacy Laws](https://mlsanity.com/paper/2206.14169/) (2022)
- [Longitudinal Analysis of Privacy Labels in the Apple App Store](https://mlsanity.com/paper/2206.02658/) (2022)
- [Tracking the Trackers: Towards Understanding the Mobile Advertising and Tracking Ecosystem](https://mlsanity.com/paper/1609.07190/) (2016)

## Abstract (arXiv)

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.