Vector-Indistinguishability: Location Dependency Based Privacy Protection for Successive Location Data

Abstract

With the wide use of GPS enabled devices and Location-Based Services, location privacy has become an increasingly worrying challenge to our community. Existing approaches provide solid bases for addressing this challenge. However, they mainly focus on independent location perturbation or whole trajectory perturbation with little attention to location dependency based perturbation between two successive locations. This may result in that perturbed successive locations lose mutual distance and direction dependency, which in turn can lead to significant data utility loss. To address this problem, we propose a new location dependency based privacy notion named as vector-indistinguishability (vector-ind). Vector-ind defines a vector to represent the dependency relationship between two successive locations. Correspondingly, it consists of distance-indistinguishability for distance dependency and direction-indistinguishability for direction dependency. Noise generation for applying Differential Privacy is based on the distance and direction, hence can reflect the location dependency to ensure data utility after perturbation. We also present four mechanisms to achieve vector-ind notion. Finally, we evaluate the empirical privacy and utility of vector-ind in real-world GPS data to demonstrate how our proposed mechanisms for vector-ind can protect location privacy with high data utility in successive location data.