Short paper: location privacy: user behavior in the field

Current smartphone platforms provide ways for users to control access to information about their location. For instance, on the iPhone, when an application requests access to location information, the operating system asks the user whether to grant location access to this application. In this paper, we study how users are using these controls. Do iPhone users allow applications to access their location? Do their decisions differ from application to application? Can we predict how a user will respond for a particular application, given their past responses for other applications? We gather data from iPhone users that sheds new light on these questions. Our results indicate that there are different classes of users: some deny all applications access to their location, some allow all applications access to their location, and some selectively permit a fraction of their applications to access their location. We also find that apps can be separated into different classes by what fraction of users trust the app with their location data. Finally, we investigate using machine learning techniques to predict users' location-sharing decisions; we find that we are sometimes able to predict the user's actual choice, though there is considerable room for improvement. If it is possible to improve the accuracy rate further, this information could be used to relieve users of the cognitive burden of individually assigning location permissions for each application, allowing users to focus their attention on more critical matters.

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