Modeling Users' Mobile App Privacy Preferences: Restoring Usability in a Sea of Permission Settings

By Jialiu Lin, Bin Liu, Norman M. Sadeh, Jason I. Hong. Symposium On Usable Privacy and Security, 2014

295 citations recorded by the source corpus.

How researchers see this paper

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Your Location has been Shared 5,398 Times!: A Field Study on Mobile App Privacy Nudging (2015, background): “Even finer grained privacy recommendations can be provided by clustering such privacy decisions into user preference profiles [28, 27].”
Static analysis of android apps: A systematic literature review (2017, background): “[91], StaDynA [114], MalloDroid [42], Relda [50], Redexer [40], CryptoLint [33], Androguard [62], Adagio [43], Brahmastra [105], Scandal [41], Mann et al.”
DEEProtect: Enabling Inference-based Access Control on Mobile Sensing Applications (2017, methodology): “For usage mode 2, we use the same criterion for specifying which inferences are acceptable as explored in previous research on permission granting [51].”
User Perceptions of Smart Home IoT Privacy (2018, background): “Crowdsourcing or otherwise automating suggested defaults for diﬀerent situations or users could arguably decrease the need for users to customize their privacy and security settings [5, 28].”