MockDroid: trading privacy for application functionality on smartphones

By Alastair R. Beresford, Andrew Colin Rice, Nicholas Skehin, R. Sohan. Workshop on Mobile Computing Systems and Applications, 2011

478 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/b41ce493cf8010de824239480f8a84440b35c8aa

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Citations in the corpus

Unsafe exposure analysis of mobile in-app advertisements (2012, background): “MockDroid [7] rewrites privacy-sensitive API calls to simu-”
DEEProtect: Enabling Inference-based Access Control on Mobile Sensing Applications (2017, background): “However, most existing SPPMs only provide binary access control over the sensor data that requires users to choose between sharing or blocking a sensor – limiting their applicability and adoption [33], [79], [7], [39].”
These aren't the droids you're looking for: retrofitting android to protect data from imperious applications (2011, background): “However, they only tested to determine if shadowing could be applied to applications without causing them to crash–they did not measure user-discernable side effects [5].”
Taming Information-Stealing Smartphone Applications (on Android) (2011, background): “MockDroid [4] allows users to mock the access from an untrusted app to particular resources at runtime (by reporting either empty or unavailable).”
RiskRanker: scalable and accurate zero-day android malware detection (2012, background): “[32] Beresford, A. R., Rice, A., Skehin, N., and Sohan, R. MockDroid: Trading Privacy for Application Functionality on Smartphones.”
Location Privacy Protection Based on Differential Privacy Strategy for Big Data in Industrial Internet of Things (2018, unknown): “[15] has been developed to deal with”
Enabling Fine-Grained Permissions for Augmented Reality Applications with Recognizers (2013, methodology): “MockDroid [3] and AppFence [14] allow using fake sensor data.”
Expectation and purpose: understanding users' mental models of mobile app privacy through crowdsourcing (2012, background): “This was quickly followed by research exploring solutions that offered users finer grain control over the use of these APIs [8-10].”
The Evolution of Android Malware and Android Analysis Techniques (2017, background): “Furthermore Mockdroid [Beresford et al. 2011] modiﬁed the OS permission checks to revoke system accesses at runtime.”
Uranine: Real-time Privacy Leakage Monitoring without System Modification for Android (2015, background): “A number of works additionally prevent access of private information or supply fake data to apps [5, 40, 23].”
Modeling Users' Mobile App Privacy Preferences: Restoring Usability in a Sea of Permission Settings (2014, background): “This was quickly followed by research exploring solutions that offered users finer grain control over the use of these APIs [8-10].”
TaintDroid (2010, unknown): “2011], MockDroid [Beresford et al. 2011], and AppFence [Hornyack et al.”
Our Twitter Profiles, Our Selves: Predicting Personality with Twitter (2011, background): “, fake geographic locations) and then share it with mobile social-networking services [4], [30].”
Detecting repackaged smartphone applications in third-party android marketplaces (2012, background): “Apex [37], MockDroid [6], and TISSA [44] enhance the Android infrastructure so that users can better control the access to specific resources or permission at runtime themselves.”
PScout: analyzing the Android permission specification (2012, methodology): “Similar to AppFence, MockDroid [8] also provide anonymized user data to applications, it demonstrates that existing applications can tolerate some form of resource failure such as lost of network and GPS signals.”