Person:
Portela García-Miguel, Javier

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First Name
Javier
Last Name
Portela García-Miguel
Affiliation
Universidad Complutense de Madrid
Faculty / Institute
Estudios estadísticos
Department
Estadística y Ciencia de los Datos
Area
Estadística e Investigación Operativa
Identifiers
UCM identifierORCIDScopus Author IDWeb of Science ResearcherIDDialnet IDGoogle Scholar ID

Search Results

Now showing 1 - 6 of 6
  • Publication
    Estimation of Anonymous Email Network Characteristics through Statistical Disclosure Attacks
    (MDPI, 2016-11-01) Portela García-Miguel, Javier; García Villalba, Luis Javier; Silva Trujillo, Alejandra Guadalupe; Sandoval Orozco, Ana Lucila; Kim, Tai-Hoon
    Social network analysis aims to obtain relational data from social systems to identify leaders, roles, and communities in order to model profiles or predict a specific behavior in users’ network. Preserving anonymity in social networks is a subject of major concern. Anonymity can be compromised by disclosing senders’ or receivers’ identity, message content, or sender-receiver relationships. Under strongly incomplete information, a statistical disclosure attack is used to estimate the network and node characteristics such as centrality and clustering measures, degree distribution, and small-world-ness. A database of email networks in 29 university faculties is used to study the method. A research on the small-world-ness and Power law characteristics of these email networks is also developed, helping to understand the behavior of small email networks.
  • Publication
    Análisis de los contenidos docentes de matemáticas en el doble grado ADE-Informática
    (2018-12-17) García Pineda, M. Pilar; Heras Martínez, Antonio José; Blanco García, Susana; Balbas Aparicio, Raquel; García Villalba, Luis Javier; Sandoval Orozco, Ana Lucila; Portela García-Miguel, Javier; Riomoros Callejo, María Isabel; Rebollo Castillo, Francisco Javier
  • Publication
    Extracting Association Patterns in Network Communications
    (MDPI, 2015-02-11) Portela García-Miguel, Javier; García Villalba, Luis Javier; Silva Trujillo, Alejandra Guadalupe; Sandoval Orozco, Ana Lucila; Kim, Tai-hoon
    In network communications, mixes provide protection against observers hiding the appearance of messages, patterns, length and links between senders and receivers. Statistical disclosure attacks aim to reveal the identity of senders and receivers in a communication network setting when it is protected by standard techniques based on mixes. This work aims to develop a global statistical disclosure attack to detect relationships between users. The only information used by the attacker is the number of messages sent and received by each user for each round, the batch of messages grouped by the anonymity system. A new modeling framework based on contingency tables is used. The assumptions are more flexible than those used in the literature, allowing to apply the method to multiple situations automatically, such as email data or social networks data. A classification scheme based on combinatoric solutions of the space of rounds retrieved is developed. Solutions about relationships between users are provided for all pairs of users simultaneously, since the dependence of the data retrieved needs to be addressed in a global sense.
  • Publication
    Análisis de la diferencia de género en el rendimiento académico en Matemáticas en los grados de ADE y FBS
    García Pineda, María Pilar; Almaraz Luengo, Elena Salome; Blanco García, Susana; De Frutos Espinosa, María Cristina; García Villalba, Luis Javier; Martínez Hernández, Luis Alberto; Pérez Arteaga, Sandra; Portela García-Miguel, Javier; Povedano Álvarez, Daniel; Rodríguez Palanquex, María Cruz; Sandoval Orozco, Ana Lucila; Turrado García, Fernando
    El objetivo es analizar con evidencias empíricas, obtenidas mediante herramientas estadísticas e informáticas, las posibles diferencias de género en el rendimiento académico en las asignaturas de Matemáticas, en grados no STEM, como son ADE y FBS
  • Publication
    Las Matemáticas Empresariales en el marco Erasmus Mundus
    (2017-12-20) García Pineda, María Pilar; Heras Martínez, Antonio José; Blanco García, Susana; Balbás Aparicio, Raquel; García Villalba, Luis Javier; Riomoros Callejo, María Isabel; Portela García-Miguel, Javier; Sandoval Orozco, Ana Lucila; Rebollo Castillo, Francisco Javier
    La creciente importancia de los métodos cuantitativos en las ciencias económicas y empresariales nos motiva a proponer una revisión detallada de los syllabus de las materias de matemáticas que se imparten en el Grado de Administración y Dirección de Empresas, con el objetivo de Investigar las correspondencias entre nuestros syllabus y los de las mas importantes universidades a nivel internacional (en el marco Erasmus Mundus). La investigación que proponemos llevará a cabo comparaciones exhaustivas de los temarios de esta categoría de asignaturas y sus metodologías docentes, y detectará las posibles discrepancias existentes en este tipo de estudios dependiendo de la universidad que los imparte. En una segunda fase, estudiaremos las causas de las posibles diferencias detectadas y, finalmente, produciremos un sistema capaz de sugerir medidas concretas que solventen los posibles problemas detectados.
  • Publication
    Learning strategies for sensitive content detection
    (MDPI, 2023-06-01) Povedano Álvarez, Daniel; Sandoval Orozco, Ana Lucila; Portela García-Miguel, Javier; García Villalba, Luis Javier; Guo, Zhenhua
    Currently, the volume of sensitive content on the Internet, such as pornography and child pornography, and the amount of time that people spend online (especially children) have led to an increase in the distribution of such content (e.g., images of children being sexually abused, real-time videos of such abuse, grooming activities, etc.). It is therefore essential to have effective IT tools that automate the detection and blocking of this type of material, as manual filtering of huge volumes of data is practically impossible. The goal of this study is to carry out a comprehensive review of different learning strategies for the detection of sensitive content available in the literature, from the most conventional techniques to the most cutting-edge deep learning algorithms, highlighting the strengths and weaknesses of each, as well as the datasets used. The performance and scalability of the different strategies proposed in this work depend on the heterogeneity of the dataset, the feature extraction techniques (hashes, visual, audio, etc.) and the learning algorithms. Finally, new lines of research in sensitive-content detection are presented.