TY - GEN
T1 - Automated analysis of privacy requirements for mobile apps
AU - Zimmeck, Sebastian
AU - Wang, Ziqi
AU - Zou, Lieyong
AU - Iyengar, Roger
AU - Liu, Bin
AU - Schaub, Florian
AU - Wilson, Shomir
AU - Sadeh, Norman
AU - Bellovin, Steven M.
AU - Reidenberg, Joel
N1 - Publisher Copyright:
Copyright © 2016, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
PY - 2016
Y1 - 2016
N2 - Mobile apps have to satisfy various privacy requirements. App publishers are often obligated to provide a privacy policy 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 activists, 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 combining 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 suggest that as many as 41 % of these apps could be collecting location information and 17% could be sharing such with third parties without disclosing so in their policies. Overall, it appears that each app exhibits a mean of 1.83 inconsistencies.
AB - Mobile apps have to satisfy various privacy requirements. App publishers are often obligated to provide a privacy policy 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 activists, 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 combining 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 suggest that as many as 41 % of these apps could be collecting location information and 17% could be sharing such with third parties without disclosing so in their policies. Overall, it appears that each app exhibits a mean of 1.83 inconsistencies.
UR - http://www.scopus.com/inward/record.url?scp=85025824519&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85025824519&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85025824519
T3 - AAAI Fall Symposium - Technical Report
SP - 286
EP - 296
BT - FS-16-01
PB - AI Access Foundation
T2 - 2016 AAAI Fall Symposium
Y2 - 17 November 2016 through 19 November 2016
ER -