Privacy and Freedom.

In this paper, we review some advances made recently in the study of mobile phone datasets. This area of research has emerged a decade ago, with the increasing availability of large-scale anonymized datasets, and has grown into a stand-alone topic. We survey the contributions made so far on the social networks that can be constructed with such data, the study of personal mobility, geographical partitioning, urban planning, and help towards development as well as security and privacy issues.

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A survey of results on mobile phone datasets analysis

Understanding human mobility is crucial for a broad range of applications from disease prediction to communication networks. Most efforts on studying human mobility have so far used private and low resolution data, such as call data records. Here, we propose Twitter as a proxy for human mobility, as it relies on publicly available data and provides high resolution positioning when users opt to geotag their tweets with their current location. We analyse a Twitter dataset with more than six million geotagged tweets posted in Australia, and we demonstrate that Twitter can be a reliable source for studying human mobility patterns. Our analysis shows that geotagged tweets can capture rich features of human mobility, such as the diversity of movement orbits among individuals and of movements within and between cities. We also find that short- and long-distance movers both spend most of their time in large metropolitan areas, in contrast with intermediate-distance movers’ movements, reflecting the impact of different modes of travel. Our study provides solid evidence that Twitter can indeed be a useful proxy for tracking and predicting human movement.

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Understanding Human Mobility from Twitter

In this paper, we address the issue of predicting the next location of an individual based on the observations of his mobility behavior over some period of time and the recent locations that he has visited. This work has several potential applications such as the evaluation of geo-privacy mechanisms, the development of location-based services anticipating the next movement of a user and the design of location-aware proactive resource migration. In a nutshell, we extend a mobility model called Mobility Markov Chain (MMC) in order to incorporate the n previous visited locations and we develop a novel algorithm for next location prediction based on this mobility model that we coined as n-MMC. The evaluation of the efficiency of our algorithm on three different datasets demonstrates an accuracy for the prediction of the next location in the range of 70% to 95% as soon as n = 2.

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Next place prediction using mobility Markov chains

Human mobility prediction is of great importance for a wide spectrum of location-based applications. However, predicting mobility is not trivial because of three challenges: 1) the complex sequential transition regularities exhibited with time-dependent and high-order nature; 2) the multi-level periodicity of human mobility; and 3) the heterogeneity and sparsity of the collected trajectory data. In this paper, we propose DeepMove, an attentional recurrent network for mobility prediction from lengthy and sparse trajectories. In DeepMove, we first design a multi-modal embedding recurrent neural network to capture the complicated sequential transitions by jointly embedding the multiple factors that govern the human mobility. Then, we propose a historical attention model with two mechanisms to capture the multi-level periodicity in a principle way, which effectively utilizes the periodicity nature to augment the recurrent neural network for mobility prediction. We perform experiments on three representative real-life mobility datasets, and extensive evaluation results demonstrate that our model outperforms the state-of-the-art models by more than 10%. Moreover, compared with the state-of-the-art neural network models, DeepMove provides intuitive explanations into the prediction and sheds light on interpretable mobility prediction.

DeepMove: Predicting Human Mobility with Attentional Recurrent Networks

Due 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 use 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 sensitive 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. We describe our experiments conducted with GEPETO for comparing different inference attacks, and evaluating their efficiency for the identification of point of interests, as well as their resilience to sanitization mechanisms such as sampling and perturbation. We also introduce a mobility model that we coin as mobility Markov Chain, which can represent in a compact yet precise way the mobility behaviour of an individual. Finally, we describe an algorithm for learning such a structure from the mobility traces of an individual and we report on experimentations performed with real mobility data.

Show Me How You Move and I Will Tell You Who You Are

De-anonymization attack on geolocated data

Due 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.

/pdf/show-me-how-you-move-and-i-will-tell-you-who-you-are-1oh4dglrvx.pdf

Show me how you move and I will tell you who you are

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.

/pdf/de-anonymization-attack-on-geolocated-data-1cl9nys35j.pdf

Miguel Nuñez del Prado Cortez

Papers

Next place prediction using mobility Markov chains

Show Me How You Move and I Will Tell You Who You Are

De-anonymization attack on geolocated data

Show me how you move and I will tell you who you are

De-anonymization Attack on Geolocated Data