Title: Optimizing noise level for perturbing geo-location data,
Abstract: With the tremendous increase in the number of smart phones, app stores have
been overwhelmed with applications requiring geo-location access in order to
provide their users better services through personalization. Revealing a user's
location to these third party apps, no matter at what frequency, is a severe
privacy breach which can have unpleasant social consequences. In order to
prevent inference attacks derived from geo-location data, a number of location
obfuscation techniques have been proposed in the literature. However, none of
them provides any objective measure of privacy guarantee. Some work has been
done to define differential privacy for geo-location data in the form of
geo-indistinguishability with l privacy guarantee. These techniques do not
utilize any prior background information about the Points of Interest (PoIs) of
a user and apply Laplacian noise to perturb all the location coordinates.
Intuitively, the utility of such a mechanism can be improved if the noise
distribution is derived after considering some prior information about PoIs.
In this paper, we apply the standard definition of differential privacy on
geo-location data. We use first principles to model various privacy and utility
constraints, prior background information available about the PoIs
(distribution of PoI locations in a 1D plane) and the granularity of the input
required by different types of apps, in order to produce a more accurate and a
utility maximizing differentially private algorithm for geo-location data at
the OS level. We investigate this for a particular category of apps and for
some specific scenarios. This will also help us to verify that whether
Laplacian noise is still the optimal perturbation when we have such prior
information.