Optimal noise functions for location privacy on continuous regions

by   Ehab ElSalamouny, et al.

Users of location-based services (LBSs) are highly vulnerable to privacy risks since they need to disclose, at least partially, their locations to benefit from these services. One possibility to limit these risks is to obfuscate the location of a user by adding random noise drawn from a noise function. In this paper, we require the noise functions to satisfy a generic location privacy notion called ℓ-privacy, which makes the position of the user in a given region X relatively indistinguishable from other points in X. We also aim at minimizing the loss in the service utility due to such obfuscation. While existing optimization frameworks regard the region X restrictively as a finite set of points, we consider the more realistic case in which the region is rather continuous with a non-zero area. In this situation, we demonstrate that circular noise functions are enough to satisfy ℓ-privacy on X and equivalently on the entire space without any penalty in the utility. Afterwards, we describe a large parametric space of noise functions that satisfy ℓ-privacy on X, and show that this space has always an optimal member, regardless of ℓ and X. We also investigate the recent notion of ϵ-geo-indistinguishability as an instance of ℓ-privacy, and prove in this case that with respect to any increasing loss function, the planar Laplace noise function is optimal for any region having a nonzero area.


Three-way optimization of privacy and utility of location data

With the recent bloom of data and the drive towards an information-based...

Local Obfuscation Mechanisms for Hiding Probability Distributions

We introduce a formal model for the information leakage of probability d...

On the Anonymization of Differentially Private Location Obfuscation

Obfuscation techniques in location-based services (LBSs) have been shown...

Geo-Graph-Indistinguishability: Location Privacy on Road Networks Based on Differential Privacy

In recent years, concerns about location privacy are increasing with the...

TACO: A Tree-based Approach to Customizing Location Obfuscation based on User Policies

A large body of literature exists for studying Location obfuscation in d...

Tagvisor: A Privacy Advisor for Sharing Hashtags

Hashtag has emerged as a widely used concept of popular culture and camp...

Location Privacy Protection Game against Adversary through Multi-user Cooperative Obfuscation

In location-based services(LBSs), it is promising for users to crowdsour...

Please sign up or login with your details

Forgot password? Click here to reset