Android permissions: user attention, comprehension, and behavior

By Adrienne Porter Felt, Elizabeth Ha, Serge Egelman, Ariel Haney, Erika Chin, David A. Wagner. Symposium On Usable Privacy and Security, 2012

1,233 citations recorded by the source corpus.

Subfields: Information Systems, Sociology and Political Science, Signal Processing

View Paper: https://www.semanticscholar.org/paper/1b9fae8255fda28e4adeb96a36f8e907e8aac6f9

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Citations in the corpus

I've got 99 problems, but vibration ain't one: a survey of smartphone users' concerns (2012, background): “Categories and Subject Descriptors D.4.6 [Operating Systems]: Security and Protection General Terms Human Factors, Security Keywords Smartphones, risks, warnings 1.”
Operating System Support for Augmented Reality Applications (2013, background): “A permission-based approach is more promising, and recent work focuses on the psychology of asking for permissions in Android and other systems [10, 11].”
Modeling Users' Mobile App Privacy Preferences: Restoring Usability in a Sea of Permission Settings (2014, background): “Several studies have examined usability issues related to the permission interface displayed to users as they download Android apps [5-7].”
Enabling Fine-Grained Permissions for Augmented Reality Applications with Recognizers (2013, background): “Because manifestbased systems have known problems with user attention [11], we are also exploring how access-control gadgets might interact with privacy goggles [27].”
Why eve and mallory love android: an analysis of android SSL (in)security (2012, methodology): “Categories and Subject Descriptors C.2.3 [Computer-Communication Network]: Network Operations—Public Networks; H.3.5 [Information Storage and Retrieval]: Online Information Services—Data Sharing Permission to make digital or hard copies of all or part of this work for personal or classroom use is…”
Your Location has been Shared 5,398 Times!: A Field Study on Mobile App Privacy Nudging (2015, background): “More recently, researchers have identified a similar information asymmetry in the context of mobile privacy: users are unaware of the data collection performed by mobile apps [14, 30].”
Spearphone: A Speech Privacy Exploit via Accelerometer-Sensed Reverberations from Smartphone Loudspeakers (2019, background): “ffects the usability of the smartphones. Even implementing the explicit usage permission model by the applications often does not work since users do not pay proper attention to the asked permissions [29]. They often do not read all required permissions, and even when reading, they are unable to und”
WHYPER: Towards Automating Risk Assessment of Mobile Applications (2013, background): “Finally, this work dovetails nicely with recent discourse concerning the appropriateness of Android per-missions to protect user security [15–18].”
End User Security and Privacy Concerns with Smart Homes (2017, background): “For smartphones, centralized app stores provide app reputation information, and prior work [25] has shown that users use these reviews to make decisions about which applications to install, including for security and privacy reasons.”
PScout: analyzing the Android permission specification (2012, background): “[18, 19] examine the effectiveness of current permission systems and show that developers are actively using the permission system to reduce the attack surface of their applications.”
SoK: Privacy on Mobile Devices – It’s Complicated (2016, background): “, GPS coordinates) will alert the user, it has been shown that most users will blindly accept these requests without much concern for how the data will be used [29].”
Scaling the Security Wall: Developing a Security Behavior Intentions Scale (SeBIS) (2015, background): “While the Westin index is well-cited and frequently used by privacy researchers, it has been shown to be a poor predictor of privacy-preserving behavior (e.g., [48, 16]).”
: Privacy on Mobile Devices – It ’ s Complicated (2016, background): “, GPS coordinates) will alert the user, it has been shown that most users will blindly accept these requests without much concern for how the data will be used [29].”
World-Driven Access Control for Continuous Sensing (2014, background): “User-driven access control [35] addresses these ﬂaws by coupling permission granting with user actions within an application (e.g., clicking a special embedded camera button).”
MADAM: Effective and Efficient Behavior-based Android Malware Detection and Prevention (2018, background): “However, both permissions and isolation mechanisms have shown weaknesses [10].”
FlowFence: Practical Data Protection for Emerging IoT Application Frameworks (2016, background): “Past work has shown that users often do not comprehend or ignore prompts [25], however, existing research does point out interesting directions for future work in improving such systems.”
Privacy as part of the app decision-making process (2013, background): “They found that most users do not pay attention to the permissions screens at install time (83%) and that only three percent of their surveyed users had a good understanding of what the permissions were actually asking for access to [9].”