Automated Analysis of Privacy Requirements for Mobile Apps

Sebastian Zimmeck, Ziqi Wang, Lieyong Zou, Roger Iyengar, Bin Liu, Florian Schaub, Shomir Wilson, Norman Sadeh, Steven M. Bellovin, Joel Reidenberg

Research output: Chapter in Book/Report/Conference proceedingChapter

87 Scopus citations


Mobile apps have to satisfy various privacy requirements. App publishers are often obligated to provide a privacy pol-icy and notify users of their apps' privacy practices. But how can we tell whether an app behaves as its policy promises? In this study we introduce a scalable system to help analyze and predict Android apps' compliance with privacy requirements. Our system is not only intended for regulators and privacy ac-tivists, but also meant to assist app publishers and app store owners in their internal assessments of privacy requirement compliance. Our analysis of 17,991 free apps shows the viability of com-bining machine learning-based privacy policy analysis with static code analysis of apps. Results suggest that 71% of apps that lack a privacy policy should have one. Also, for 9,050 apps that have a policy, we find many instances of potential inconsistencies between what the app policy seems to state and what the code of the app appears to do. Our results sug-gest that as many as 41% of these apps could be collecting lo-cation information and 17% could be sharing such with third parties without disclosing so in their policies. Overall, it ap-pears that each app exhibits a mean of 1.83 inconsistencies.
Original languageEnglish (US)
Title of host publicationProceedings 2017 Network and Distributed System Security Symposium
Place of PublicationReston, VA
PublisherKorea Society of Internet Information
ISBN (Print)1-891562-46-0
StatePublished - 2017

Publication series

NameProceedings 2017 Network and Distributed System Security Symposium


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