Geo-indistinguishability for location traces (PETS2014 in real time)

16 July 2014

The paper “A Predictive Differentially-Private Mechanism for Mobility Traces” looks at sanitizing mobility traces within the paradigm of differential privacy.

The idea is that a user wishes to use a location-based service. However, a user also wishes to maintain some privacy, so they will somewhat distort the reported location to hide their exact location. Of course, there is a clear trade-off between the utility, namely the accuracy of the reported location, and the privacy that can be maintained. In the case of this work the utility is related with the accuracy of the reported location compared with the real location.

At the heart of the system lies a definition of privacy based on “geo-indistinguishability”. The insight is that the locations “close-by” need to be indistinguishable, while locations very far apart may be distinguished. This offers a higher degree of information leakage than traditional differential privacy, but stands a chance to offer some utility for a single user trace.

A previously proposed mechanism offers such privacy, through first perturbing the center point of the user location (using a 2D generalization of the Laplacian distribution) and the requesting information about a larger mechanism. Its privacy degrades in a predictable manner when multiple observations are seen by the adversary. However, the authors note that real-world traces are very correlated with each other. For example a user stays in a cafe for a while, or they follow a certain path on a motorway. This insight may be used to reduce the noise introduced by the mechanism.

First a straw man mechanism is proposed: a prediction function is defined that tries to predict the next position to report from the previous published position. If the prediction yields a “good enough” location, subject to some threshold, the old prediction is used. If not a new prediction is used, at the cost to the privacy budget used. However, this simple scheme breaks the definition of geo-indistinguishability.

The strawman mechanism can be strengthened by doing a private test for the accuracy of the prediction, which in itself consumes some amount of the privacy budget. This results in information not leaking from the private prediction test on the data, and yields a geo-indistringuishable mechanism.

To make the mechanism more easy to integrate into location-based services a privacy budget manager is also devised. The manager is provided with a certain target privacy level and utility, and will configure the parameters of the mechanism to offer good utility subject to the constraints.

The evaluation was gone on the GeoLife traces from MSR, that were processed to simulate queries to an LBS, through sub-sampling. 

Interestingly, a plug-in for Firefox and Chrome is available that implements the approach.


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