Comparison of Feature Extraction Methods and Predictors for Income Inference

Abstract—Patterns of mobile phone communications, coupled with the information of the social network graph and financial behavior, allow us to make inferences of users’ socio-economic attributes such as their income level. We present here several methods to extract features from mobile phone usage (calls and messages), and compare different combinations of supervised machine learning techniques and sets of features used as input for the inference of users’ income. Our experimental results show that the Bayesian method based on the communication graph outperforms standard machine learning algorithms using nodebased features.Sociedad Argentina de Informática e Investigación Operativa (SADIO

Comparison of Feature Extraction Methods and Predictors for Income Inference

Abstract—Patterns of mobile phone communications, coupled with the information of the social network graph and financial behavior, allow us to make inferences of users’ socio-economic attributes such as their income level. We present here several methods to extract features from mobile phone usage (calls and messages), and compare different combinations of supervised machine learning techniques and sets of features used as input for the inference of users’ income. Our experimental results show that the Bayesian method based on the communication graph outperforms standard machine learning algorithms using nodebased features.Sociedad Argentina de Informática e Investigación Operativa (SADIO

Inference of Socioeconomic Status in a Communication Graph

In this work, we examine the socio-economic correlations present among users in a mobile phone network in Mexico. First, we find that the distribution of income for a subset of users –for which we have income information given by a large bank in Mexico– follows closely, but not exactly, the income distribution for the whole population of Mexico. We also show the existence of a strong socio-economic homophily in the mobile phone network, where users linked in the network are more likely to have similar income. The main contribution of this work is that we leverage this homophily in order to propose a methodology, based on Bayesian statistics, to infer the socio-economic status for a large subset of users in the network (for which we have no banking information). With our proposed algorithm, we achieve an accuracy of 0.71 in a two-class classification problem (low and high income) which significantly outperforms a simpler method based on a frequentist approach. Finally, we extend the two-class classification problem to multiple classes by using the Dirichlet distribution.Sociedad Argentina de Informática e Investigación Operativa (SADIO

Secure Exchange of Digital Goods in a Decentralized Data Marketplace

We are tackling the problem of trading real-world private information using only cryptographic protocols and a public blockchain to guarantee honest transactions. In this project, we consider three types of agents —buyers, sellers and notaries— interacting in a decentralized privacy-preserving data marketplace (dPDM) such as theWibson data marketplace. This framework offers infrastructure and financial incentives for individuals to securely sell personal information while preserving personal privacy. Here we provide an efficient cryptographic primitive for the secure exchange of data in a dPDM, which occurs as an atomic operation wherein the data buyer gets access to the data and the data seller gets paid simultaneously.Sociedad Argentina de Informática e Investigación Operativ

Secure Exchange of Digital Goods in a Decentralized Data Marketplace

We are tackling the problem of trading real-world private information using only cryptographic protocols and a public blockchain to guarantee honest transactions. In this project, we consider three types of agents —buyers, sellers and notaries— interacting in a decentralized privacy-preserving data marketplace (dPDM) such as theWibson data marketplace. This framework offers infrastructure and financial incentives for individuals to securely sell personal information while preserving personal privacy. Here we provide an efficient cryptographic primitive for the secure exchange of data in a dPDM, which occurs as an atomic operation wherein the data buyer gets access to the data and the data seller gets paid simultaneously.Sociedad Argentina de Informática e Investigación Operativ

Uncovering the Spread of an Infectious 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. Finally, we show the value of mobile phone records to predict long-term migrations, which play a crucial role in the spread of Chagas disease.Sociedad Argentina de Informática e Investigación Operativa (SADIO

Uncovering the Spread of an Infectious 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. Finally, we show the value of mobile phone records to predict long-term migrations, which play a crucial role in the spread of Chagas disease.Sociedad Argentina de Informática e Investigación Operativa (SADIO

Secure Exchange of Digital Goods in a Decentralized Data Marketplace

We are tackling the problem of trading real-world private information using only cryptographic protocols and a public blockchain to guarantee honest transactions. In this project, we consider three types of agents —buyers, sellers and notaries— interacting in a decentralized privacy-preserving data marketplace (dPDM) such as theWibson data marketplace. This framework offers infrastructure and financial incentives for individuals to securely sell personal information while preserving personal privacy. Here we provide an efficient cryptographic primitive for the secure exchange of data in a dPDM, which occurs as an atomic operation wherein the data buyer gets access to the data and the data seller gets paid simultaneously.Sociedad Argentina de Informática e Investigación Operativ

Uncovering the Spread of an Infectious 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. Finally, we show the value of mobile phone records to predict long-term migrations, which play a crucial role in the spread of Chagas disease.Sociedad Argentina de Informática e Investigación Operativa (SADIO