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Privacy policies disclose how an organization collects and handles personalinformation. Recent work has made progress in leveraging natural languageprocessing (NLP) to automate privacy policy analysis and extract datacollection statements from different sentences, considered in isolation fromeach other. In this paper, we view and analyze, for the first time, the entiretext of a privacy policy in an integrated way. In terms of methodology: (1) wedefine PoliGraph, a type of knowledge graph that captures statements in apolicy as relations between different parts of the text; and (2) we revisit thenotion of ontologies, previously defined in heuristic ways, to capturesubsumption relations between terms. We make a clear distinction between localand global ontologies to capture the context of individual policies,application domains, and privacy laws. We develop PoliGrapher, an NLP tool toautomatically extract PoliGraph from the text using linguistic analysis. Usinga public dataset for evaluation, we show that PoliGrapher identifies 40% morecollection statements than prior state-of-the-art, with 97% precision. In termsof applications, PoliGraph enables automated analysis of a corpus of policiesand allows us to: (1) reveal common patterns in the texts across differentpolicies, and (2) assess the correctness of the terms as defined within apolicy. We also apply PoliGraph to: (3) detect contradictions in a policy,where we show false alarms by prior work, and (4) analyze the consistency ofpolicies and network traffic, where we identify significantly more cleardisclosures than prior work. Finally, leveraging the capabilities of theemerging large language models (LLMs), we also present PoliGrapher-LM, a toolthat uses LLM prompting instead of NLP linguistic analysis, to extractPoliGraph from the policy text, and we show that it further improves coverage.

PoliGraph: Automated Privacy Policy Analysis using Knowledge Graphs (Journal Version)