Tech Reports | EECS at UC Berkeley

Andrew Fisher

EECS Department, University of California, Berkeley

Technical Report No. UCB/EECS-2012-247

December 14, 2012

http://www2.eecs.berkeley.edu/Pubs/TechRpts/2012/EECS-2012-247.pdf

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, and seek to answer the following questions:

<ul> <li>Do iPhone users allow applications to access their location?</li> <li>Do their decisions differ from application to application?</li> <li>Can we predict how a user will respond for a particular application, given their past responses for other applications?</li> </ul>

We gather data from iPhone users that sheds new light on these questions. Our results indicate that there are different classes of users:

<ul> <li>some deny all applications access to their location</li> <li>some allow all applications access to their location</li> <li>some selectively permit a fraction of their applications to access their location</li> </ul>

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.

Advisors: David Wagner and Björn Hartmann

BibTeX citation:

@mastersthesis{Fisher:EECS-2012-247,
    Author= {Fisher, Andrew},
    Title= {Location Privacy: User Behavior in the Field},
    School= {EECS Department, University of California, Berkeley},
    Year= {2012},
    Month= {Dec},
    Url= {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2012/EECS-2012-247.html},
    Number= {UCB/EECS-2012-247},
    Abstract= {<p>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, and seek to answer the following
questions:</p>

<ul>
<li>Do iPhone users allow applications to access their location?</li>
<li>Do their decisions differ from application to application?</li>
<li>Can we predict how a user will respond for a particular
application, given their past responses for other applications?</li>
</ul>

<p>We gather data from iPhone users that sheds new light on these questions.
Our results indicate that there are different classes of users:<p>

<ul>
<li>some deny all applications access to their location</li>
<li>some allow all applications access to their location</li>
<li>some selectively permit a fraction of their applications to access their location</li>
</ul>

<p>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.</p>},
}

EndNote citation:

%0 Thesis
%A Fisher, Andrew 
%T Location Privacy: User Behavior in the Field
%I EECS Department, University of California, Berkeley
%D 2012
%8 December 14
%@ UCB/EECS-2012-247
%U http://www2.eecs.berkeley.edu/Pubs/TechRpts/2012/EECS-2012-247.html
%F Fisher:EECS-2012-247