UNDERSTANDING AND CAPTURING PEOPLE’S MOBILE APP PRIVACY PREFERENCES

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UNDERSTANDING AND CAPTURING PEOPLE’S MOBILE APP PRIVACY PREFERENCES (2013)

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by Jialiu Lin , Mahadev Satyanarayanan , Consolvo Google

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BibTeX

@MISC{Lin13understandingand,
    author = {Jialiu Lin and Mahadev Satyanarayanan and Consolvo Google},
    title = {UNDERSTANDING AND CAPTURING PEOPLE’S MOBILE APP PRIVACY PREFERENCES},
    year = {2013}
}

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Abstract

Users are increasingly expected to manage a wide range of security and privacy settings. An important example of this trend is the variety of users might be called upon to review permissions when they download mobile apps. Experiments have shown that most users struggle with reviewing these permissions. Earlier research efforts in this area have primarily focused on protecting users ’ privacy and security through the development of analysis tools and extensions intended to further increase the level of control provided to users with little regard for human factor considerations. This thesis aims to address this gap through the study of user mobile app privacy preferences with the dual objective of both simplifying and enhancing mobile app privacy decision interfaces. Specifically, we combine static code analysis, crowdsourcing and machine learning techniques to elicit people’s mobile app privacy preferences. We show how the resulting preference models can inform the design of interfaces that offer the promise of alleviating user burden when it comes to reviewing the permissions requested by mobile apps. Our contribution is threefold. First, we provide the first large-scale, indepth

Keyphrases

mobile apps earlier research effort static code analysis mobile app privacy preference dual objective little regard user mobile app privacy preference important example analysis tool human factor consideration mobile app privacy decision interface wide range preference model user burden privacy setting user privacy

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