The Curious Case of the PDF Converter that Likes Mozart: Dissecting and Mitigating the Privacy Risk of Personal Cloud Apps
Hamza Harkous, Rameez Rahman, Bojan Karlas, Karl Aberer

TL;DR
This paper analyzes privacy risks in personal cloud apps, revealing over-privilege issues, and introduces a novel permission model called Far-reaching Insights that effectively educates users and reduces over-privileged app installations.
Contribution
It proposes and evaluates a new ensemble permission model, Far-reaching Insights, which improves user understanding and reduces privacy risks in third-party cloud apps.
Findings
Over two thirds of analyzed apps are over-privileged.
Far-reaching Insights doubles the effectiveness in discouraging over-privileged app installation.
Deployment of a privacy-oriented app store demonstrates practical benefits.
Abstract
Third party apps that work on top of personal cloud services such as Google Drive and Dropbox, require access to the user's data in order to provide some functionality. Through detailed analysis of a hundred popular Google Drive apps from Google's Chrome store, we discover that the existing permission model is quite often misused: around two thirds of analyzed apps are over-privileged, i.e., they access more data than is needed for them to function. In this work, we analyze three different permission models that aim to discourage users from installing over-privileged apps. In experiments with 210 real users, we discover that the most successful permission model is our novel ensemble method that we call Far-reaching Insights. Far-reaching Insights inform the users about the data-driven insights that apps can make about them (e.g., their topics of interest, collaboration and activity…
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