# PrivSTRUCT: Untangling Data Purpose Compliance of Privacy Policies in Google Play Store

arXiv: https://arxiv.org/abs/2604.22157 · HTML: https://mlsanity.com/paper/2604.22157/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 31 references that are in the map.
- First posted on arXiv 24 Apr 2026.

## Builds on (most cited first, 14 of 14)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://mlsanity.com/paper/2305.18290/) (2023)
- [Retrieval-Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2312.10997/) (2023)
- [Polisis: Automated Analysis and Presentation of Privacy Policies Using Deep Learning](https://mlsanity.com/paper/1802.02561/) (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)
- [The Privacy Policy Landscape After the GDPR](https://mlsanity.com/paper/1809.08396/) (2018)
- [PolicyGPT: Automated Analysis of Privacy Policies with Large Language Models](https://mlsanity.com/paper/2309.10238/) (2023)
- [We Value Your Privacy ... Now Take Some Cookies: Measuring the GDPR's Impact on Web Privacy](https://mlsanity.com/paper/1808.05096/) (2018)
- [PoliGraph: Automated Privacy Policy Analysis using Knowledge Graphs (Journal Version)](https://mlsanity.com/paper/2210.06746/) (2022)
- [Unpacking Privacy Labels: A Measurement and Developer Perspective on Google's Data Safety Section](https://mlsanity.com/paper/2306.08111/) (2023)
- [Automating Governing Knowledge Commons and Contextual Integrity (GKC-CI) Privacy Policy Annotations with Large Language Models](https://mlsanity.com/paper/2311.02192/) (2023)
- [Entailment-Driven Privacy Policy Classification with LLMs](https://mlsanity.com/paper/2409.16621/) (2024)
- [PrivPRISM: Automatically Detecting Discrepancies Between Google Play Data Safety Declarations and Developer Privacy Policies](https://mlsanity.com/paper/2603.09214/) (2026)

## Abstract (arXiv)

Existing research typically treats privacy policies as flat, uniform text, extracting information without regard for the document's logical hierarchy. Disregard for structural cues of section headings designed to guide the reader, often leads automated methods to entangle distinct data practices, particularly when linking sensitive data items to their specific purposes. To address this, we introduce PrivSTRUCT, a novel and systematic encoder and decoder combined framework that to untangle complex privacy disclosures. Benchmarking against the state-of-the-art tool PoliGrapher reveals that PrivSTRUCT robustly extracts more than x2 the number of data item and purpose excerpts while retaining developer-defined structural cues. By applying PrivSTRUCT to a large-scale dataset of 3,756 Android apps, we uncover a critical transparency gap: the probability of developers overstating a data purpose is 20.4% higher for first-party collection and 9.7% higher for third-party sharing when they rely on globally defined purposes rather than specific, locally scoped disclosures. Alarmingly, we find that sensitive third-party data flows such as sharing financial data for analytics are frequently diluted and entangled into generic or unrelated categories, highlighting a persistent failure in the current purpose disclosure landscape.