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International audienceDue to the emergence of geolocated applications, more and more mobility traces are generated on a daily basis and collected in the form of geolocated datasets. If an unauthorized entity can access this data, it can used it to infer personal information about the individuals whose movements are contained within these datasets, such as learning their home and place of work or even their social network, thus causing a privacy breach. In order to protect the privacy of individuals, a sanitization process, which adds uncertainty to the data and removes some sensible information, has to be performed. The global objective of GEPETO (for GEoPrivacy Enhancing TOolkit) is to provide researchers concerned with geo-privacy with means to evaluate various sanitization techniques and inference attacks on geolocated data. In this paper, we report on our preliminary experiments with GEPETO for comparing different clustering algorithms and heuristics that can be used as inference attacks, and evaluate their efficiency for the identification of point of interests, as well as their resilience to sanitization mechanisms such as sampling and perturbation

Abstract-With the advent of GPS-equipped devices, a massive amount of location data is being collected, raising the issue of the privacy risks incurred by the individuals whose movements are recorded. In this work, we focus on a specific inference attack called the de-anonymization attack, by which an adversary tries to infer the identity of a particular individual behind a set of mobility traces. More specifically, we propose an implementation of this attack based on a mobility model called Mobility Markov Chain (MMC). A MMC is built out from the mobility traces observed during the training phase and is used to perform the attack during the testing phase. We design two distance metrics quantifying the closeness between two MMCs and combine these distances to build de-anonymizers that can re-identify users in an anonymized geolocated dataset. Experiments conducted on real datasets demonstrate that the attack is both accurate and resilient to sanitization mechanisms such as downsampling.

We show how the quantum paradigm can be used to speed up unsupervised learning algorithms. More precisely, we explain how it is possible to accelerate learning algorithms by quantizing some of their subroutines. Quantization refers to the process that partially or totally converts a classical algorithm to its quantum counterpart in order to improve performance. In particular, we give quantized versions of clustering via minimum spanning tree, divisive clustering and k-medians that are faster than their classical analogues. We also describe a distributed version of k-medians that allows the participants to save on the global communication cost of the protocol compared to the classical version. Finally, we design quantum algorithms for the construction of a neighbourhood graph, outlier detection as well as smart initialization of the cluster centres.

The breadcrumbs we leave behind when using our mobile phones—who somebody calls, for how long, and from where—contain unprecedented insights about us and our societies. Researchers have compared the recent availability of large-scale behavioral datasets, such as the ones generated by mobile phones, to the invention of the microscope, giving rise to the new field of computational social science.

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We present the first protocol for the anonymous transmission of a quantum state that is information-theoretically secure against an active adversary, without any assumption on the number of corrupt participants. The anonymity of the sender and receiver, as well as the privacy of the quantum state, are perfectly protected except with exponentially small probability. Even though a single corrupt participant can cause the protocol to abort, the quantum state can only be destroyed with exponentially small probability: if the protocol succeeds, the state is transferred to the receiver and otherwise it remains in the hands of the sender (provided the receiver is honest).

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