Yves-Alexandre de Montjoye

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We study fifteen months of human mobility data for one and a half million individuals and find that human mobility traces are highly unique. In fact, in a dataset where the location of an individual is specified hourly, and with a spatial resolution equal to that given by the carrier's antennas, four spatio-temporal points are enough to uniquely identify(More)
Although widely used in practice, the behavior and accuracy of the popular module identification technique called modularity maximization is not well understood in practical contexts. Here, we present a broad characterization of its performance in such situations. First, we revisit and clarify the resolution limit phenomenon for modularity maximization.(More)
The D4D-Senegal challenge is an open innovation data challenge on anonymous call patterns of Orange's mobile phone users in Senegal. The goal of the challenge is to help address society development questions in novel ways by contributing to the socioeconomic development and well-being of the Senegalese population. Participants to the challenge are given(More)
—We present a comparative analysis of the behav-ioral dynamics of rural and urban societies using four years of mobile phone data from all 1.4M subscribers within a small country. We use information from communication logs and top up denominations to characterize attributes such as socioeconomic status and region. We show that rural and urban communities(More)
Numerous studies have documented the normal age-related decline of neural structure, function, and cognitive performance. Preliminary evidence suggests that meditation may reduce decline in specific cognitive domains and in brain structure. Here we extended this research by investigating the relation between age and fluid intelligence and resting state(More)
The present study provides the first evidence that personality can be reliably predicted from standard mobile phone logs. Using a set of novel psychology-informed indicators that can be computed from data available to all carriers, we were able to predict users' personality with a mean accuracy across traits of 42% better than random, reaching up to 61%(More)
Large-scale data sets of human behavior have the potential to fundamentally transform the way we fight diseases, design cities, or perform research. Metadata, however, contain sensitive information. Understanding the privacy of these data sets is key to their broad use and, ultimately, their impact. We study 3 months of credit card records for 1.1 million(More)
Large-scale personal data has become the new oil of the Internet. However, personal data tend to be monopolized and siloed by online services which not only impedes beneficial uses but also prevents users from managing the risks associated with their data. While there is substantial legal and social policy scholarship concerning ownership and fair use of(More)
The rise of smartphones and web services made possible the large-scale collection of personal metadata. Information about individuals' location, phone call logs, or web-searches, is collected and used intensively by organizations and big data researchers. Metadata has however yet to realize its full potential. Privacy and legal concerns, as well as the lack(More)