Optimizing Noise Level for Perturbing Geo-location Data
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 thir
- PDF / 529,244 Bytes
- 11 Pages / 439.37 x 666.142 pts Page_size
- 56 Downloads / 200 Views
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 (PoI s) 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 PoI s. 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 PoI s (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 whether Laplacian noise is still the optimal perturbation when we have such prior information. Keywords: Differential privacy · Utility Geo-location data · Laplacian noise
1
· Points of interest
Introduction
Over the years, a number of mobile phone services are becoming dependent on user’s location in order to provide a better experience, be it a dating app, restaurant search, nearby gas stations lookup and what not. All these services require a user to surrender her location (mostly exact coordinates) in order c Springer Nature Switzerland AG 2019 K. Arai et al. (Eds.): FICC 2018, AISC 887, pp. 63–73, 2019. https://doi.org/10.1007/978-3-030-03405-4_5
64
A. Palia and R. Tandon
to derive accurate results. With the increasing popularity of social networks, extracting auxiliary information about an individual has become easier than ever before. Both of these factors have increased the likelihood of inference attacks on the users which can have unpleasant social consequences. Therefore, revealing a user’s location, no matter at what frequency, is a severe privacy breach [5]. The criticality of geo-location data can be estimated by the news pieces reporting that the Egyptian government used to locate and imprison users of Grindr–a gay dating app [4]. Grindr uses geo-location of its users in order to provide them a perfect match in their vicinity. Most of the users have submit
Data Loading...
