mlsanityData to 7 Oct 2026

PrivPRISM: Automatically Detecting Discrepancies Between Google Play Data Safety Declarations and Developer Privacy Policies

arXiv 2603.09214 ↗First posted 10 Mar 2026cs.AIcited by 1

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Abstract (arXiv)

End-users seldom read verbose privacy policies, leading app stores like Google Play to mandate simplified data safety declarations as a user-friendly alternative. However, these self-declared disclosures often contradict the full privacy policies, deceiving users about actual data practices and violating regulatory requirements for consistency. To address this, we introduce PrivPRISM, a robust framework that combines encoder and decoder language models to systematically extract and compare fine-grained data practices from privacy policies and to compare against data safety declarations, enabling scalable detection of non-compliance. Evaluating 7,770 popular mobile games uncovers discrepancies in nearly 53% of cases, rising to 61% among 1,711 widely used generic apps. Additionally, static code analysis reveals possible under-disclosures, with privacy policies disclosing just 66.8% of potential accesses to sensitive data like location and financial information, versus only 36.4% in data safety declarations of mobile games. Our findings expose systemic issues, including widespread reuse of generic privacy policies, vague / contradictory statements, and hidden risks in high-profile apps with 100M+ downloads, underscoring the urgent need for automated enforcement to protect platform integrity and for end-users to be vigilant about sensitive data they disclose via popular apps.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 15 of its 43 references that are in the map. First posted on arXiv 10 Mar 2026.

Builds on

15 of 43 references.

References · 15

  1. 2024The Llama 3 Herd of Modelscited by 14,192
  2. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,185
  3. 2018Polisis: Automated Analysis and Presentation of Privacy Policies Using Deep Learningcited by 50
  4. 2024CLEAR: Towards Contextual LLM-Empowered Privacy Policy Analysis and Risk Generation for Large Language Model Applicationscited by 33
  5. 2020Privacy Policies over Time: Curation and Analysis of a Million-Document Datasetcited by 28
  6. 2020Privacy at Scale: Introducing the PrivaSeer Corpus of Web Privacy Policiescited by 22
  7. 2018The Privacy Policy Landscape After the GDPRcited by 18
  8. 2023PolicyGPT: Automated Analysis of Privacy Policies with Large Language Modelscited by 16
  9. 2018We Value Your Privacy ... Now Take Some Cookies: Measuring the GDPR's Impact on Web Privacycited by 15
  10. 2022PoliGraph: Automated Privacy Policy Analysis using Knowledge Graphs (Journal Version)cited by 10
  11. 2023Unpacking Privacy Labels: A Measurement and Developer Perspective on Google's Data Safety Sectioncited by 6
  12. 2025"You don't need a university degree to comprehend data protection this way": LLM-Powered Interactive Privacy Policy Assessmentcited by 5
3 more
  1. 2023Automating Governing Knowledge Commons and Contextual Integrity (GKC-CI) Privacy Policy Annotations with Large Language Modelscited by 3
  2. 2024Entailment-Driven Privacy Policy Classification with LLMscited by 2
  3. 2024Do Android App Developers Accurately Report Collection of Privacy-Related Data?cited by 1

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