TL;DR
This paper assesses privacy risks of analytics libraries in popular Android apps and introduces a user-controlled data anonymization method, MobHide, demonstrated through the HideDroid app to balance data utility and privacy.
Contribution
It provides an empirical privacy analysis of analytics in Android apps and proposes a novel, user-controlled anonymization approach called MobHide.
Findings
Analytics libraries collect extensive user data in popular apps.
The MobHide methodology enables user-controlled data anonymization.
HideDroid effectively demonstrates the anonymization approach.
Abstract
Mobile applications (hereafter, apps) collect a plethora of information regarding the user behavior and his device through third-party analytics libraries. However, the collection and usage of such data raised several privacy concerns, mainly because the end-user - i.e., the actual owner of the data - is out of the loop in this collection process. Also, the existing privacy-enhanced solutions that emerged in the last years follow an "all or nothing" approach, leaving the user the sole option to accept or completely deny the access to privacy-related data. This work has the two-fold objective of assessing the privacy implications on the usage of analytics libraries in mobile apps and proposing a data anonymization methodology that enables a trade-off between the utility and privacy of the collected data and gives the user complete control over the sharing process. To achieve that, we…
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