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Is It a Trap? A Large-scale Empirical Study And Comprehensive Assessment of Online Automated Privacy Policy Generators for Mobile Apps

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arxiv 2305.03271 v2 pith:TK45L225 submitted 2023-05-05 cs.SE cs.CR

Shidong Pan , Dawen Zhang , Mark Staples , Zhenchang Xing , Jieshan Chen , Xiwei Xu , James Hoang This is my paper · ORCID
classification cs.SEcs.CR
keywords privacyappgspoliciesappsmobilepolicyassessmentautomated
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Privacy regulations protect and promote the privacy of individuals by requiring mobile apps to provide a privacy policy that explains what personal information is collected and how these apps process this information. However, developers often do not have sufficient legal knowledge to create such privacy policies. Online Automated Privacy Policy Generators (APPGs) can create privacy policies, but their quality and other characteristics can vary. In this paper, we conduct the first large-scale empirical study and comprehensive assessment of APPGs for mobile apps. Specifically, we scrutinize 10 APPGs on multiple dimensions. We further perform the market penetration analysis by collecting 46,472 Android app privacy policies from Google Play, discovering that nearly 20.1% of privacy policies could be generated by existing APPGs. Lastly, we point out that generated policies in our study do not fully comply with GDPR, CCPA, or LGPD. In summary, app developers must carefully select and use the appropriate APPGs with careful consideration to avoid potential pitfalls.

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  1. SoK: From Generation to Consumption of Privacy Documents in Software Systems

    cs.CR 2026-08 conditional novelty 6.0 of 10

    A systematic review of 290 papers (2010 to 2025) organizes privacy-document research into a five-stage lifecycle and identifies 15 trends, 21 opportunities, and 4 research directions.

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