Optimizing noise level for perturbing geo-location data

TL;DR

This paper develops a differentially private algorithm for geo-location data that leverages prior information about Points of Interest to improve utility while maintaining privacy guarantees.

Contribution

It introduces a utility-maximizing differentially private mechanism for geo-location data that considers prior PoI information, improving over existing Laplacian noise methods.

Findings

The proposed method enhances utility by incorporating prior PoI distribution.

Laplacian noise may not be optimal when prior PoI information is available.

The approach provides a more accurate privacy-utility trade-off for specific app scenarios.

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.