Carlos Sarraute

A study of age and gender seen through mobile phone usage patterns in Mexico

Mobile phone usage provides a wealth of information, which can be used to better understand the demographic structure of a population. In this paper we focus on the population of Mexican mobile phone users. Our first contribution is an observational study of mobile phone usage according to gender and age groups. We were able to detect significant differences in phone usage among different subgroups of the population. Our second contribution is to provide a novel methodology to predict demographic features (namely age and gender) of unlabeled users by leveraging individual calling patterns, as well as the structure of the communication graph. We provide details of the methodology and show experimental results on a real world dataset that involves millions of users.

Simulating cyber-attacks for fun and profit

We introduce a new simulation platform called Insight, created to design and simulate cyber-attacks against large arbitrary target scenarios. Insight has surprisingly low hardware and configuration requirements, while making the simulation a realistic experience from the attackers standpoint. The scenarios include a crowd of simulated actors: network devices, hardware devices, software applications, protocols, users, etc. A novel characteristic of this tool is to simulate vulnerabilities (including 0-days) and exploits, allowing an attacker to compromise machines and use them as pivoting stones to continue the attack. A user can test and modify complex scenarios, with several interconnected networks, where the attacker has no initial connectivity with the objective of the attack. We give a concise description of this new technology, and its possible uses in the security research field, such as pen-testing training, study of the impact of 0-days vulnerabilities, evaluation of security countermeasures, and risk assessment tool.

On the Regularity of Human Mobility

Understanding human mobility patterns is crucial to fields such as urban mobility and mobile network planning. For this purpose, we make use of large-scale datasets recording individuals spatio-temporal locations, from eight major world cities: Beijing, Tokyo, New York, Paris, San Francisco, London, Moscow and Mexico City. Our contributions are two-fold: first, we show significant similarities in peoples mobility habits regardless of the city and nature of the dataset. Second, we unveil three persistent traits present in an individuals urban mobility: repetitiveness, preference for shortest-paths, and confinement. These characteristics uncover peoples tendency to revisit few favourite venues using the shortest-path available.

Correlations of consumption patterns in social-economic networks

We analyze a coupled dataset collecting the mobile phone communication and bank transactions history of a large number of individuals living in Mexico. After mapping the social structure and introducing indicators of socioeconomic status, demographic features, and purchasing habits of individuals we show that typical consumption patterns are strongly correlated with identified socioeconomic classes leading to patterns of stratification in the social structure. In addition we measure correlations between merchant categories and introduce a correlation network, which emerges with a meaningful community structure. We detect multivariate relations between merchant categories and show correlations in purchasing habits of individuals. Our work provides novel and detailed insight into the relations between social and consuming behaviour with potential applications in recommendation system design.

Analyzing the spread of chagas disease with mobile phone data

We use mobile phone records for the analysis of mobility patterns and the detection of possible risk zones of Chagas disease in two Latin American countries. We show that geolocalized call records are rich in social and individual information, which can be used to infer whether an individual has lived in an endemic area. We present two case studies, in Argentina and in Mexico, using data provided by mobile phone companies from each country. The risk maps that we generate can be used by health campaign managers to target specific areas and allocate resources more effectively.

Inference of demographic attributes based on mobile phone usage patterns and social network topology

AbstractMobile phone usage provides a wealth of information, which can be used to better understand the demographic structure of a population. In this paper, we focus on the population of Mexican mobile phone users. We first present an observational study of mobile phone usage according to gender and age groups. We are able to detect significant differences in phone usage among different subgroups of the population. We then study the performance of different machine learning (ML) methods to predict demographic features (namely, age and gender) of unlabeled users by leveraging individual calling patterns, as well as the structure of the communication graph. We show how a specific implementation of a diffusion model, harnessing the graph structure, has significantly better performance over other node-based standard ML methods. We provide details of the methodology together with an analysis of the robustness of our results to changes in the model parameters. Furthermore, by carefully examining the topological relations of the training nodes (seed nodes) to the rest of the nodes in the network, we find topological metrics which have a direct influence on the performance of the algorithm.

Correlations and dynamics of consumption patterns in social-economic networks

We analyse a coupled dataset collecting the mobile phone communications and bank transactions history of a large number of individuals living in a Latin American country. After mapping the social structure and introducing indicators of socioeconomic status, demographic features, and purchasing habits of individuals, we show that typical consumption patterns are strongly correlated with identified socioeconomic classes leading to patterns of stratification in the social structure. In addition, we measure correlations between merchant categories and introduce a correlation network, which emerges with a meaningful community structure. We detect multivariate relations between merchant categories and show correlations in purchasing habits of individuals. Finally, by analysing individual consumption histories, we detect dynamical patterns in purchase behaviour and their correlations with the socioeconomic status, demographic characters and the egocentric social network of individuals. Our work provides novel and detailed insight into the relations between social and consuming behaviour with potential applications in resource allocation, marketing, and recommendation system design.

Towards an adaptive completion of sparse Call Detail Records for mobility analysis

Call Detail Records (CDRs) are a primary source of whereabouts in the study of multiple mobility-related aspects. However, the spatiotemporal sparsity of CDRs often limits their utility in terms of the dependability of results. In this paper, driven by real-world data across a large population, we propose two approaches for completing CDRs adaptively, to reduce the sparsity and mitigate the problems the latter raises. Owing to high-precision sampling, the comparative evaluation shows that our approaches outperform the legacy solution in the literature in terms of the combination of accuracy and temporal coverage. Also, we reveal those important factors for completing sparse CDR data, which sheds lights on the design of similar approaches.

Mobile data traffic modeling

This paper presents a detailed measurement-driven model of mobile data traffic usage of smartphone subscribers, using a large-scale dataset collected from a major 3G network in a dense metropolitan area. Our main contribution is a synthetic, measurement-based, mobile data traffic generator capable of simulating traffic-related activity patterns over time for different categories of subscribers and time periods for a typical day in their lives. We first characterize individual subscribers routinary behavior, followed by a detailed investigation of subscribers temporal usage patterns (i.e., when and how much traffic is generated). We then classify the subscribers into six distinct profiles according to their usage patterns and model these profiles according to two daily time periods: peak and non-peak hours. We show that the synthetic trace generated by our data traffic model consistently replicates a subscribers profiles for these two time periods when compared to the original dataset. Broadly, our observations bring important insights into temporal network resource usage. We also discuss relevant issues in traffic demands and describe implications in network solution evaluation and privacy.

Taking Benefit from the User Density in Large Cities for Delivering SMS

In this paper, from a measurement study and analysis of SMS based on traces coming from a nationwide cellular telecommunication operator during a two month period, we propose a DTN (Delay Tolerant Network) like network protocol for delivering SMS. More precisely, we perform a temporal and spatial analysis of the Mexico City cellular network considering geolocalized SMS. The temporal analysis allows us to detect events and to check for overloading periods, with abnormal or unexpected traffic, and to study the evolution of classical parameters such as activity or distance between source and destination. The spatial analysis is based on the Voronoï diagram of the base stations covering Mexico City. We explain how SMS traffic can be characterized. Such key characterization allows us to answer the question: is it possible to transmit SMS using phones as relay in a large city such as Mexico City? We defined a simple network protocol to transmit SMS from a source to a destination. This DTN like protocol does not need routing nor global knowledge. The protocol takes benefit from the locality of SMS, the density of phones in Mexico City and the mobility of phone users. We studied a mobile dataset including 8 millions users living in Mexico city. This gave use a precise estimation of the average transmission time and the global performance of our approach. After 30 minutes, half of the SMS were delivered successfully to destination.