USBDI - Artículos de Revistas

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  • Publication
    Towards social fairness in smart policing: Leveraging territorial, racial, and workload fairness in the police districting problem
    (Elsevier Ltd., 2023-06-01) Liberatore, Federico; Camacho-Collados, Miguel; Quijano Sánchez, Lara; Ministerio de Ciencia e Innovación (España)
    Recent events (e.g., George Floyd protests) have shown the impact that inequality in policing can have on society. Thus, police operations should be planned and designed taking into account the interests of three main groups of directly affected stakeholders (i.e., general population, minorities, and police agents) to pursue fairness. Most models presented so far in the literature failed at this, optimizing cost efficiency or operational effectiveness instead while disregarding other social goals. In this paper, a Smart Policing model that produces operational patrolling districts and includes territorial, racial, and workload fairness criteria is proposed. The patrolling configurations are designed according to the territorial distribution of crime risk and population subgroups, while equalizing the total risk exposure across the districts, according to the preferences of a decision-maker. The model is formulated as a multi-objective mixed-integer program. Computational experiments on randomly generated data are used to empirically draw insights into the relationship between the fairness criteria considered. Finally, the model is tested and validated on a real-world dataset about the Central District of Madrid (Spain). Experiments show that the model identifies solutions that dominate the current patrolling configuration used.
  • Publication
    SocialHaterBERT: A dichotomous approach for automatically detecting hate speech on Twitter through textual analysis and user profiles
    (Elsevier, 2023-04-15) Valle Cano, Gloria del; Quijano Sánchez, Lara; Liberatore, Federico; Gómez, Jesús; European Commission; Ministerio de Ciencia e Innovación (España)
    Social media platforms have evolved into an online representation of our social interactions. We may use the resources they provide to analyze phenomena that occur within them, such as the development and viralization of offensive and hostile content. In today's polarized world, the escalating nature of this behavior is cause for concern in modern society. This research includes an in-depth examination of previous efforts and strategies for detecting and preventing hateful content on the social network Twitter, as well as a novel classification approach based on users' profiles, related social environment and generated tweets. This paper's contribution is threefold: (i) an improvement in the performance of the HaterNet algorithm, an expert system developed in collaboration with the Spanish National Office Against Hate Crimes of the Spanish State Secretariat for Security (Ministry of the Interior) that is capable of identifying and monitoring the evolution of hate speech on Twitter using an LTSM + MLP neural network architecture. To that end, a model based on BERT, HaterBERT, has been created and tested using HaterNet's public dataset, providing results that show a significant improvement; (ii) A methodology to create a user database in the form of a relational network to infer textual and centrality features. This contribution, SocialGraph, has been independently tested with various traditional Machine Learning and Deep Learning algorithms, demonstrating its usefulness in spotting haters; (iii) a final model, SocialHaterBERT, that integrates the previous two approaches by analyzing features other than those inherent in the text. Experiment results reveal that this last contribution greatly improves outcomes, establishing a new field of study that transcends textual boundaries, paving the way for future research in coupled models from a diachronic and dynamic perspective.
  • Publication
    A fast epigraph and hypograph-based approach for clustering functional data
    (Springer, 2023-04-01) Pulido Bravo, Belén; Franco Pereira, Alba María; Lillo Rodríguez, Rosa Elvira; Ministerio de Ciencia e Innovación (España)
    Clustering techniques for multivariate data are useful tools in Statistics that have been fully studied in the literature. However, there is limited literature on clustering methodologies for functional data. Our proposal consists of a clustering procedure for functional data using techniques for clustering multivariate data. The idea is to reduce a functional data problem into a multivariate one by applying the epigraph and hypograph indexes to the original curves and to their first and/or second derivatives. All the information given by the functional data is therefore transformed to the multivariate context, being informative enough for the usual multivariate clustering techniques to be efficient. The performance of this new methodology is evaluated through a simulation study and is also illustrated through real data sets. The results are compared to some other clustering procedures for functional data.
  • Publication
    A Generative Angular Model of Protein Structure Evolution
    (Oxford University Press, 2017-08-01) Golden, Michael; García Portugués, Eduardo; Sorensen, Michael; Mardia, Kanti V.; Hamelryck, Thomas; Hein, Jontun; Ministerio de Economía y Competitividad (España)
    Recently described stochastic models of protein evolution have demonstrated that the inclusion of structural information in addition to amino acid sequences leads to a more reliable estimation of evolutionary parameters. We present a generative, evolutionary model of protein structure and sequence that is valid on a local length scale. The model concerns the local dependencies between sequence and structure evolution in a pair of homologous proteins. The evolutionary trajectory between the two structures in the protein pair is treated as a random walk in dihedral angle space, which is modeled using a novel angular diffusion process on the two-dimensional torus. Coupling sequence and structure evolution in our model allows for modeling both "smooth" conformational changes and "catastrophic" conformational jumps, conditioned on the amino acid changes. The model has interpretable parameters and is comparatively more realistic than previous stochastic models, providing new insights into the relationship between sequence and structure evolution. For example, using the trained model we were able to identify an apparent sequence-structure evolutionary motif present in a large number of homologous protein pairs. The generative nature of our model enables us to evaluate its validity and its ability to simulate aspects of protein evolution conditioned on an amino acid sequence, a related amino acid sequence, a related structure or any combination thereof.
  • Publication
    Evolution and study of a copycat effect in intimate partner homicides: A lesson from Spanish femicides
    (PLOS, 2019-06) Torrecilla Noguerales, José Luis; Quijano Sánchez, Lara; Liberatore, Federico; López Ossorio, Juan J.; González Álvarez, José L.; Comunidad de Madrid; Ministerio de Economía y Competitividad (España)
    Objectives: This paper focuses on the issue of intimate partner violence and, specifically, on the distribution of femicides over time and the existence of copycat effects. This is the subject of an ongoing debate often triggered by the social alarm following multiple intimate partner homicides (IPHs) occurring in a short span of time. The aim of this research is to study the evolution of IPHs and provide a far-reaching answer by rigorously analyzing and searching for patterns in data on femicides. Methods: The study analyzes an official dataset, provided by the system VioGen of the Secretarla de Estado de Seguridad (Spanish State Secretariat for Security), including all the femicides occurred in Spain in 2007-2017. A statistical methodology to identify temporal interdependencies in count time series is proposed and applied to the dataset. The same methodology can be applied to other contexts. Results: There has been a decreasing trend in the number of femicides per year. No interdependencies among the temporal distribution of femicides are observed. Therefore, according to data, the existence of copycat effect in femicides cannot be claimed. Conclusions: Around 2011 there was a clear change in the average number of femicides which has not picked up. Results allow for an informed answer to the debate on copycat effect in Spanish femicides. The planning of femicides prevention activities should not be a reaction to a perceived increase in their occurrence. As a copycat effect is not detected in the studied time period, there is no evidence supporting the need to censor media reports on femicides.
  • Publication
    Air temperature forecasting using machine learning techniques: a review
    (MDPI, 2020-08-02) Cifuentes Quintero, Jenny Alexandra; Marulanda, Geovanny; Bello, Antonio; Reneses, Javier
    Efforts to understand the influence of historical climate change, at global and regional levels, have been increasing over the past decade. In particular, the estimates of air temperatures have been considered as a key factor in climate impact studies on agricultural, ecological, environmental, and industrial sectors. Accurate temperature prediction helps to safeguard life and property, playing an important role in planning activities for the government, industry, and the public. The primary aim of this study is to review the different machine learning strategies for temperature forecasting, available in the literature, presenting their advantages and disadvantages and identifying research gaps. This survey shows that Machine Learning techniques can help to accurately predict temperatures based on a set of input features, which can include the previous values of temperature, relative humidity, solar radiation, rain and wind speed measurements, among others. The review reveals that Deep Learning strategies report smaller errors (Mean Square Error = 0.0017 °K) compared with traditional Artificial Neural Networks architectures, for 1 step-ahead at regional scale. At the global scale, Support Vector Machines are preferred based on their good compromise between simplicity and accuracy. In addition, the accuracy of the methods described in this work is found to be dependent on inputs combination, architecture, and learning algorithms. Finally, further research areas in temperature forecasting are outlined.
  • Publication
    Wind power long-term scenario generation considering spatial-temporal dependencies in coupled electricity markets
    (MDPI, 2020-07-01) Marulanda, Geovanny; Bello, Antonio; Cifuentes Quintero, Jenny Alexandra; Reneses, Javier
    Wind power has been increasing its participation in electricity markets in many countries around the world. Due to its economical and environmental benefits, wind power generation is one of the most powerful technologies to deal with global warming and climate change. However, as wind power grows, uncertainty in power supply increases due to wind intermittence. In this context, accurate wind power scenarios are needed to guide decision-making in power systems. In this paper, a novel methodology to generate realistic wind power scenarios for the long term is proposed. Unlike most of the literature that tackles this problem, this paper is focused on the generation of realistic wind power production scenarios in the long term. Moreover, spatial-temporal dependencies in multi-area markets have been considered. The results show that capturing the dependencies at the monthly level could improve the quality of scenarios at different time scales. In addition, an evaluation at different time scales is needed to select the best approach in terms of the distribution functions of the generated scenarios. To evaluate the proposed methodology, several tests have been made using real data of wind power generation for Spain, Portugal and France.
  • Publication
    Shuffle, cut, and learn: Crypto Go, a card game for teaching cryptography
    (MDPI, 2020-11) González-Tablas Ferreres, Ana Isabel; González Vasco, María Isabel; Cascos Fernández, Ignacio; Planet Palomino, Alvaro; Ministerio de Economía y Competitividad (España)
    Cryptography is the mathematical core of information security. It serves both as a source of hard computational problems and as precise language allowing for the formalization of sound security models. While dealing with the mathematical foundations of cybersecurity is only possible in specialized courses (tertiary level and beyond), it is essential to promote the role of mathematics in this field at early educational stages. With this in mind, we introduce Crypto Go, a physical card game that may be used both as a dissemination and as an educational tool. The game is carefully devised in order to entertain and stimulate players, while boosting their understanding on how basic cryptographic tools work and interplay. To get a preliminary assessment of our design, we collected data from a series of test workshops, which engaged over two hundred players from different ages and educational backgrounds. This basic evaluation indeed confirms that Crypto Go significantly improves students' motivation and has a positive impact in their perception and understanding of the field.
  • Publication
    A mathematical pre-disaster model with uncertainty and multiple criteria for facility location and network fortification
    (MPDI, 2020-04-03) Monzon, Julia; Liberatore, Federico; Vitoriano, Begoña; European Commission; Ministerio de Economía, Industria y Competitividad (España)
    Disasters have catastrophic effects on the affected population, especially in developing and underdeveloped countries. Humanitarian Logistics models can help decision-makers to efficiently and effectively warehouse and distribute emergency goods to the affected population, to reduce casualties and suffering. However, poor planning and structural damage to the transportation infrastructure could hamper these efforts and, eventually, make it impossible to reach all the affected demand centers. In this paper, a pre-disaster Humanitarian Logistics model is presented that jointly optimizes the prepositioning of aid distribution centers and the strengthening of road sections to ensure that as much affected population as possible can efficiently get help. The model is stochastic in nature and considers that the demand in the centers affected by the disaster and the state of the transportation network are random. Uncertainty is represented through scenarios representing possible disasters. The methodology is applied to a real-world case study based on the 2018 storm system that hit the Nampula Province in Mozambique.
  • Publication
    Competing for congestible goods: experimental evidence on parking choice
    (Nature Research, 2020-11-30) Pereda, María; Ozaita, Juan; Stavrakakis, Ioannis; Sánchez, Angel; Comunidad de Madrid; Ministerio de Ciencia, Innovación y Universidades (España)
    Congestible goods describe situations in which a group of people share or use a public good that becomes congested or overexploited when demand is low. We study experimentally a congestible goods problem of relevance for parking design, namely how people choose between a convenient parking lot with few spots and a less convenient one with unlimited space. We fnd that the Nash equilibrium predicts reasonably well the competition for the convenient parking when it has few spots, but not when it has more availability. We then show that the Rosenthal equilibrium, a boundedrational approach, is a better description of the experimental results accounting for the randomness in the decision process. We introduce a dynamical model that shows how Rosenthal equilibria can be approached in a few rounds of the game. Our results give insights on how to deal with parking problems such as the design of parking lots in central locations in cities and open the way to better understand similar congestible goods problems in other contexts.
  • Publication
    Conductors' tempo choices shed light over Beethoven's metronome
    (PLOS, 2020-12-16) Martin-Castro, Almudena; Úcar Marques, Iñaki
    During most part of Western classical music history, tempo, the speed of music, was not specified, for it was considered obvious from musical context. Only in 1815, Maelzel patented the metronome. Beethoven immediately embraced it, so much as to add tempo marks to his already published eight symphonies. However, these marks are still under dispute, as many musicians consider them too quick to be played and even unmusical, whereas others claim them as Bethoven’s supposedly written will. In this work, we develop a methodology to extract and analyze the performed tempi from 36 complete symphonic recordings by different conductors. Our results show that conductor tempo choices reveal a systematic deviation from Beethoven’s marks, which highlights the salience of “correct tempo” as a perceptive phenomenon shaped by cultural context. The hasty nature of these marks could be explained by the metronome’s ambiguous scale reading point, which Beethoven probably misinterpreted.
  • Publication
    Robots, labor markets, and universal basic income
    (Springer Nature, 2020-12-16) Cabrales Goitia, Antonio; Hernández, Penélope; Sánchez, Angel; Comunidad de Madrid; Ministerio de Ciencia e Innovación (España); Universidad Carlos III de Madrid
    Automation is a big concern in modern societies in view of its widespread impact on many socioeconomic issues including income, jobs, and productivity. While previous studies have concentrated on determining the effects on jobs and salaries, our aim is to understand how automation affects productivity, and how some policies, such as taxes on robots or universal basic income, moderate or aggravate those effects. To this end, we have designed an experiment where workers make productive effort decisions, and managers can choose between workers and robots to do these tasks. In our baseline treatment, we measure the effort made by workers who may be replaced by robots, and also elicit firm replacement decisions. Subsequently, we carry out treatments in which workers have a universal basic income of about a fifth of the workers' median wages, or where there is a tax levy on firms who replace workers by robots. We complete the picture of the impact of automation by looking into the coexistence of workers and robots with part-time jobs. We find that the threat of a robot substitution does not affect the amount of effort exerted by workers. Also, neither universal basic income nor a tax on robots decrease workers' effort. We observe that the robot substitution tax reduces the probability of worker substitution. Finally, workers that benefit from managerial decisions to not substitute them by more productive robots do not increase their effort level. Our conclusions shed light on the interplay of policy and workers behavior under pervasive automation.
  • Publication
    Comprehensive assessment, review, and comparison of AI models for solar irradiance prediction based on diferent time/estimation intervals
    (Nature Research, 2022-06-10) Bamisile, Olusola; Cai, Dongsheng; Oluwasanmi, Ariyo; Ejiyi, Chukwebuka; Ukwuoma, Chiagoziem C.; Ojo, Oluwasegun Taiwo; Mukhtar, Mustapha; Huang, Qi
    Solar energy-based technologies have developed rapidly in recent years, however, the inability to appropriately estimate solar energy resources is still a major drawback for these technologies. In this study, eight different artificial intelligence (AI) models namely; convolutional neural network (CNN), artificial neural network (ANN), long short-term memory recurrent model (LSTM), eXtreme gradient boost algorithm (XG Boost), multiple linear regression (MLR), polynomial regression (PLR), decision tree regression (DTR), and random forest regression (RFR) are designed and compared for solar irradiance prediction. Additionally, two hybrid deep neural network models (ANN-CNN and CNN-LSTM-ANN) are developed in this study for the same task. This study is novel as each of the AI models developed was used to estimate solar irradiance considering different timesteps (hourly, every minute, and daily average). Also, different solar irradiance datasets (from six countries in Africa) measured with various instruments were used to train/test the AI models. With the aim to check if there is a universal AI model for solar irradiance estimation in developing countries, the results of this study show that various AI models are suitable for different solar irradiance estimation tasks. However, XG boost has a consistently high performance for all the case studies and is the best model for 10 of the 13 case studies considered in this paper. The result of this study also shows that the prediction of hourly solar irradiance is more accurate for the models when compared to daily average and minutes timestep. The specific performance of each model for all the case studies is explicated in the paper.
  • Publication
    Ethnic markers and the emergence of group-specific norms
    (Nature Research, 2020-12-17) Ozaita Corral, Juan; Baronchelli, Andrea; Sánchez, Angel; Comunidad de Madrid; Ministerio de Ciencia e Innovación (España)
    Observable social traits determine how we interact meaningfully in society even in our globalized world. While a popular hypothesis states that observable traits may help promote cooperation, the alternative explanation that they facilitate coordination has gained ground in recent years. Here we explore this possibility and present a model that investigates the role of ethnic markers in coordination games. In particular, we aim to test the role of reinforcement learning as the microscopic mechanism used by the agents to update their strategies in the game. For a wide range of parameters, we observe the emergence of a collective equilibrium in which markers play an assorting role. However, if individuals are too conformist or too greedy, markers fail to shape social interactions. These results extend and complement previous work focused on agent imitation and show that reinforcement learning is a good candidate to explain many instances where ethnic markers influence coordination.
  • Publication
    Structural measures of personal networks predict migrants' cultural backgrounds: an explanation from Grid/Group theory
    (Oxford Academic, 2022-09) Molina, José Luis; Ozaita Corral, Juan; Tamarit, Ignacio; Sánchez, Angel; McCarty, Christopher; Bernard, H. Russell; Comunidad de Madrid; Ministerio de Economía y Competitividad (España); Universidad Carlos III de Madrid
    Culture and social structure are not separated analytical domains but intertwined phenomena observable in personal networks. Drawing on a personal networks dataset of migrants in the United States and Spain, we show that the country of origin, a proxy for diverse languages and cultural institutions, and religion may be predicted by specific combinations of personal network structural measures (closeness, clustering, betweenness, average degree, etc). We obtain similar results applying three different methods (a multinomial logistic regression, a Random Forest algorithm, and an artificial neural network). This finding is explained within the framework of the Grid/Group theory that has long posed the interdependence of social structural and cultural features of human groups.
  • Publication
    A new methodology to measure faultlines at scale leveraging digital traces
    (Springer, 2022-07-07) Mehrjoo, Amir Reza; Cuevas Rumín, Rubén; Cuevas Rumín, Ángel; Comunidad de Madrid; European Commission; Ministerio de Ciencia e Innovación (España)
    The definition of society is tight with human group-level behavior. Group faultlines defined as hypothetical lines splitting groups into homogeneous subgroups based on members' attributes have been proposed as a theoretical method to identify conflicts within groups. For instance, crusades and women's rights protests are the consequences of strong faultlines in societies with diverse cultures. Measuring the presence and strength of faultlines represents an important challenge. Existing literature resorts in questionnaires as traditional tool to find group-level behavioral attributes and thus identify faultlines. However, questionnaire data usually come with limitations and biases, especially for large-scale human group-level research. On top of that, questionnaires limit faultline research due to the possibility of dishonest answers, unconscientious responses, and differences in understanding and interpretation. In this paper, we propose a new methodology for measuring faultlines in large-scale groups, which leverages data readily available from online social networks' marketing platforms. Our methodology overcomes the limitations of traditional methods to measure group-level attributes and group faultlines at scale. To prove the applicability of our methodology, we analyzed the faultlines between people living in Spain, grouped by geographical regions. We collected data on 67,270 interest topics from Facebook users living in Spain, France, Germany, Greece, Italy, Portugal, and the United Kingdom. We computed existing metrics to measure faultlines' distance and strenght using our data to identify potential faultlines existing among Spanish regions. The results reveal that the strongest faultlines in Spain belong to Spanish Islands (the Canary Islands and the Balearic Islands), Catalonia, and Basque regions. These findings are aligned with the historical secessionist movements and cultural diversity reports supporting the validity of our methodology.
  • Publication
    Estimating ideology and polarization in European countries using Facebook data
    (Springer, 2022-11-22) Caravaca Crespo, Francisco; González Cabañas, José; Cuevas Rumín, Ángel; Cuevas Rumín, Rubén; Comunidad de Madrid; European Commission; Ministerio de Ciencia e Innovación (España); Universidad Carlos III de Madrid
    Researchers have studied political ideology and polarization in many different contexts since their effects are usually closely related to aspects and actions of individuals and societies. Hence, being able to estimate and measure the changes in political ideology and polarization is crucial for researchers, stakeholders, and the general public. In this paper, we model the ideology and polarization of 28 countries (the 27 EU member states plus the UK) using Facebook public posts from political parties’ Facebook pages. We collected a three-year dataset from 2019 to 2021 with information from 234 political parties’ Facebook pages and took advantage of the EU parliament elections of May 2019 to create our models. Our methodology works across 28 countries and benefits from being a low-cost running process that measures ideology and polarization at a high-resolution time scale. The results show our models are pretty accurate when validating them against 19 individual countries’ elections as ground truth. Moreover, to make our results available to the research community, stakeholders, and individuals interested in politics, the last contribution of our paper is a website including detailed information about the political parties in our dataset. It also includes the temporal evolution of our ideology and polarization estimations. Therefore, our work delivers a novel tool that uses Facebook public data to create country metrics useful for different purposes. To the best of our knowledge, there is no prior work in the literature offering a solution that measures the ideology and polarization of all EU + UK countries.
  • Publication
    Establishing trust in online advertising with signed transactions
    (IEEE, 2020-12-24) Pastor Valles, Antonio Ángel; Cuevas Rumín, Rubén; Cuevas Rumín, Ángel; Azcorra Saloña, Arturo; European Commission; Ministerio de Economía y Competitividad (España)
    Programmatic advertising operates one of the most sophisticated and efficient service platforms on the Internet. However, the complexity of this ecosystem is a direct cause of one of the most important problems in online advertising, the lack of transparency. This lack of transparency enables subsequent problems such as advertising fraud, which causes billions of dollars in losses. In this paper we propose Ads.chain, a technological solution to the lack-of-transparency problem in programmatic advertising. Ads.chain extends the current effort of the Internet Advertising Bureau (IAB) in providing traceability in online advertising through the Ads.txt and Ads.cert solutions, addressing the limitations of these techniques. Ads.chain is (to the best of the authors' knowledge) the first solution that provides end-to-end cryptographic traceability at the ad transaction level. It is a communication protocol that can be seamlessly embedded into ad-tags and the OpenRTB protocol, the de-facto standards for communications in online advertising, allowing an incremental adoption by the industry. We have implemented Ads.chain and made the code publicly available. We assess the performance of Ads.chain through a thorough analysis in a lab environment that emulates a real ad delivery process at real-life throughputs. The obtained results show that Ads.chain can be implemented with limited impact on the hardware resources and marginal delay increments at the publishers lower than 0.20 milliseconds per ad space on webpages and 2.6 milliseconds at the programmatic advertising platforms. These results confirm that Ads.chain's impact on the user experience and the overall operation of the programmatic ad delivery process can be considered negligible.
  • Publication
    How resilient is the open web to the COVID-19 pandemic?
    (Elsevier, 2021-11) González Cabañas, José; Callejo Pinardo, Patricia; Vallina Rodríguez, Pelayo; Cuevas Rumín, Ángel; Cuevas Rumín, Rubén; Fernández Anta, Antonio; Comunidad de Madrid; European Commission; Ministerio de Economía y Competitividad (España); Ministerio de Educación, Cultura y Deporte (España); Ministerio de Ciencia e Innovación (España)
    In this paper we refer to the Open Web to the set of services offered freely to Internet users, representing a pillar of modern societies. Despite its importance for society, it is unknown how the COVID-19 pandemic is affecting the Open Web. In this paper, we address this issue, focusing our analysis on Spain, one of the countries which have been most impacted by the pandemic. On the one hand, we study the impact of the pandemic in the financial backbone of the Open Web, the online advertising business. To this end, we leverage concepts from Supply–Demand economic theory to perform a careful analysis of the elasticity in the supply of ad-spaces to the financial shortage of the online advertising business and its subsequent reduction in ad spaces’ price. On the other hand, we analyze the distribution of the Open Web composition across business categories and its evolution during the COVID-19 pandemic. These analyses are conducted between Jan 1st and Dec 31st, 2020, using a reference dataset comprising information from more than 18 billion ad spaces. Our results indicate that the Open Web has experienced a moderate shift in its composition across business categories. However, this change is not produced by the financial shortage of the online advertising business, because as our analysis shows, the Open Web’s supply of ad spaces is inelastic (i.e., insensitive) to the sustained low-price of ad spaces during the pandemic. Instead, existing evidence suggests that the reported shift in the Open Web composition is likely due to the change in the users’ online behavior (e.g., browsing and mobile apps utilization patterns).
  • Publication
    Digital contact tracing: large-scale geolocation data as an alternative to bluetooth-based Apps failure
    (MDPI, 2021-05-05) González Cabañas, José; Cuevas Rumín, Ángel; Cuevas Rumín, Rubén; Maier, Martin; Comunidad de Madrid; European Commission; Ministerio de Economía, Industria y Competitividad (España); Ministerio de Educación, Cultura y Deporte (España); Agencia Estatal de Investigación (España)
    The currently deployed contact-tracing mobile apps have failed as an efficient solution in the context of the COVID-19 pandemic. None of them have managed to attract the number of active users required to achieve efficient operation. This urges the research community to re-open the debate and explore new avenues to lead to efficient contact-tracing solutions. In this paper, we contribute to this debate with an alternative contact-tracing solution that leverages the already available geolocation information owned by BigTech companies that have large penetration rates in most of the countries adopting contact-tracing mobile apps. Our solution provides sufficient privacy guarantees to protect the identity of infected users as well as to preclude Health Authorities from obtaining the contact graph from individuals.