AdDroid: privilege separation for applications and advertisers in Android

By P. Pearce, Adrienne Porter Felt, Gabriel Nunez, David A. Wagner. ACM Asia Conference on Computer and Communications Security, 2012

327 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering, Signal Processing

View Paper: https://www.semanticscholar.org/paper/2314d47ad81470465562d5be9ab32a4498de903b

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In Computer Networks and Communications

In Electrical and Electronic Engineering

In Signal Processing

Citations in the corpus

Reliable Third-Party Library Detection in Android and its Security Applications (2016, methodology): “Given the high prevalence of third-party libraries in apps and consequently their high impact on the health of the entire smartphone ecosystem, it is of no surprise that dedicated research has investigated new mechanisms to sandbox or remove libs, with a strong focus on advertisement libs [31, 27, 4”
Modeling Users' Mobile App Privacy Preferences: Restoring Usability in a Sea of Permission Settings (2014, background): “A number of research prototypes have also offered used fine grain controls over the permissions [8, 10, 32-35].”
Free for All! Assessing User Data Exposure to Advertising Libraries on Android (2016, background): “This, in turn, led to a substantial line of research on privacy and advertising on mobiles in two general areas: (1) strategies for detection and prevention [53], [43], [17], [31], [33], [18], [50], [52], [7], [47], [48], [35], and (2) architectures and protocols that improve privacy protections [19”
An empirical study of cryptographic misuse in android applications (2013, background): “, [25]), we observed the pervasive use of third-party libraries for advertisement and statistics purposes.”
Expectation and purpose: understanding users' mental models of mobile app privacy through crowdsourcing (2012, methodology): “Researchers have also developed many useful techniques and tools to detect the sensitive information leakage in mobile apps [3, 10, 12-16, 19, 35, 36], by using permission analysis (e.g. [3, 16]), static code analysis (e.g. [12]), network analysis (e.g. [35]), or dynamic flow analysis (e.g. [14]).”