Protecting Locations with Differential Privacy under Temporal Correlations

By Yonghui Xiao, Li Xiong. Conference on Computer and Communications Security, 2014

404 citations recorded by the source corpus.

Concerns on location privacy frequently arise with the rapid development of GPS enabled devices and location-based applications. While spatial transformation techniques such as location perturbation or generalization have been studied extensively, most techniques rely on syntactic privacy models without rigorous privacy guarantee. Many of them only consider static scenarios or perturb the location at single timestamps without considering temporal correlations of a moving user's locations, and hence are vulnerable to various inference attacks. While differential privacy has been accepted as a standard for privacy protection, applying differential privacy in location based applications presents new challenges, as the protection needs to be enforced on the fly for a single user and needs to incorporate temporal correlations between a user's locations. In this paper, we propose a systematic solution to preserve location privacy with rigorous privacy guarantee. First, we propose a new definition, "δ-location set" based differential privacy, to account for the temporal correlations in location data. Second, we show that the well known l1-norm sensitivity fails to capture the geometric sensitivity in multidimensional space and propose a new notion, sensitivity hull, based on which the error of differential privacy is bounded. Third, to obtain the optimal utility we present a planar isotropic mechanism (PIM) for location perturbation, which is the first mechanism achieving the lower bound of differential privacy. Experiments on real-world datasets also demonstrate that PIM significantly outperforms baseline approaches in data utility.

Subfields: Artificial Intelligence, Computer Science Applications, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/ba231bd280bf2c0bf2e337d2b2405df8901aace1

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Citations in the corpus

REACT (2020, methodology): “Currently, we have adopted and evaluated the geo-indistinguishability (GeoInd) privacy definition [4, 42, 39, 17] to enable users to protect their locations.”
Utility Cost of Formal Privacy for Releasing National Employer-Employee Statistics (2017, background): “For instance, it is known [33, 40, 41] that attacker-independent privacy notions like -differential privacy limit the ability of an attacker to learn properties of individual records in the input dataset if and only if the adversary believes records in the data are independent of one another.”
Differentially private publication of location entropy (2016, background): “Recent studies on location privacy have focused on leveraging differential privacy (DP) to protect the privacy of users [21, 27].”