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Analysing the effectiveness of watermarking techniques for Mixnets, This Master's thesis delves into the Nym project, a decentralized privacy tool designed to improve online anonymity. The study combines theoretical analysis with practical testing using custom scripts, providing a thorough exploration of Nym's inner workings. Our primary focus centers on mixnodes and gateways, examining their roles within the network. By simplifying complex processes, from setting up mixnodes to running client components, we uncover valuable insights. Nym holds significant potential for protecting user privacy by preventing traffic analysis and identity correlation attacks. This research contributes to improving online privacy protections. In summary, this concise abstract offers a glimpse into a comprehensive study that highlights Nym's capabilities and its potential to enhance online privacy., Esta tesis de Maestría profundiza en el proyecto Nym, una herramienta de privacidad descentralizada diseñada para mejorar el anonimato en línea. El estudio combina análisis teóricos con pruebas prácticas utilizando scripts personalizados, proporcionando una exploración exhaustiva del funcionamiento interno de Nym. Nuestro enfoque principal se centra en los mixnodes y las gateways, examinando sus roles dentro de la red. Al simplificar procesos complejos, desde la configuración de mixnodes hasta la ejecución de componentes cliente, descubrimos conocimientos valiosos. Nym posee un potencial significativo para proteger la privacidad del usuario al prevenir el análisis de tráfico y los ataques de correlación de identidad. Esta investigación contribuye a mejorar las protecciones de privacidad en línea. Para concluir, este breve resumen presenta una visión general de un estudio exhaustivo que destaca las capacidades de Nym y su potencial para aumentar la privacidad en línea., Aquesta tesi de Màster s'endinsa en el projecte Nym, una eina de privacitat descentralitzada dissenyada per millorar l'anonimat en línia. L'estudi combina anàlisi teòrics amb proves pràctiques utilitzant scripts personalitzats, proporcionant una exploració exhaustiva del funcionament intern de Nym. El nostre enfocament principal es centra en els mixnodes i les gateways, examinant els seus rols dins de la xarxa. Simplificant processos complexos, des de la configuració de mixnodes fins a l'execució de components clients, descobrim coneixements valuosos. Nym té un potencial significatiu per protegir la privacitat de l'usuari evitant l'anàlisi de trànsit i els atacs de correlació d'identitat. Aquesta investigació contribueix a millorar les proteccions de privacitat en línia. Per concloure, aquest breu resum presenta una visió general d'un estudi exhaustiu que destaca les capacitats de Nym i el seu potencial per augmentar la privacitat en línia.

The project focuses on the technological transformation of a company specialized in data management, shifting from proprietary servers to a hybrid multicloud network. This transformation involves studying the options and challenges of the multicloud strategy, using Equinix and Interxion tools to manage the virtual and physical networks that will connect with cloud providers such as AWS, Azure, and GCP. It also involves designing the network and analysing network downtime scenarios to ensure continuity. The project will be theoretically developed but based on a real-world context, with confidential details preserved. The goal is to enhance efficiency, informed decision-making, and information security in the transition to a hybrid multicloud infrastructure., El proyecto se enfoca en la transformación tecnológica de una empresa especializada en la gestión de datos, pasando de servidores propios a una red multicloud híbrida. Esta transformación implica analizar las opciones y desafíos de la estrategia multicloud, utilizando la herramienta de Equinix e Interxion para administrar las redes virtuales y físicas que se conectarán con proveedores de la nube como AWS, Azure y GCP. También implica diseñar la red y analizar casos de caídas de la red para garantizar la continuidad. El proyecto se desarrollará teóricamente, pero se basará en un contexto real, aunque se mantendrán los detalles confidenciales. El objetivo es mejorar la eficiencia, la toma de decisiones informadas y la seguridad de la información en la transición hacia una infraestructura multicloud híbrida., El projecte se centra en la transformació tecnològica d'una empresa especialitzada en la gestió de dades, passant de servidors propis a una xarxa multicloud híbrida. Aquesta transformació implica estudiar les opcions i reptes de l'estratègia multicloud, utilitzar l'eina d'Equinix i Interxion per gestionar les xarxes virtuals i físiques que connectaran amb proveïdors de cloud com AWS, Azure i GCP. També implica dissenyar la xarxa i analitzar casos de caigudes de xarxa per garantir la continuïtat. El projecte es desenvoluparà teòricament, però es basarà en un context real, tot i que es mantindran els detalls confidencials. L'objectiu és millorar l'eficiència, la presa de decisions informades i la seguretat de la informació per en el pas cap a una infraestructura multicloud híbrida.

Descripció del recurs: 19 juny 2024, El professorat de l’assignatura Transmissió de Dades, impartida en la UPC, hem elaborat aquest llibre de problemes resolts representatius d’aquesta. El llibre s’estructura en tres temes: codificació de font, criptografia i codificació de canal. La transmissió de dades és el conjunt de tècniques i conceptes que sorgeixen en estudiar el problema de la transmissió d’informació digital, qualsevol que sigui el seu origen, a través d’un canal limitat en amplada de banda i potència. La codificació de font contempla la compressió de les fonts de dades a partir del concepte d’informació. Els objectius principals als quals serveix la criptografia són la confidencialitat, la integritat i l’autenticitat en el tractament de la informació en format electrònic. Quan l’índex d’error del sistema de transmissió sense codificar és massa alt, és necessari recórrer a tècniques de codificació de canal, per a detectar errors i realitzar una retransmissió de les dades, o per a corregir-los., 1

© 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works., Continuous data publishing aims to anonymise the next publication of changing microdata while preserving privacy. The microdata can change between publications via additions, deletions, insertions, and updates. There are numerous proposals for different database types, adversaries, attacks, and notions. However, many anonymization algorithms include notions of privacy and adversarial models that are specific to the context, with their own terminology and notation. Unfortunately, these proposals are difficult to generalize or translate them to other contexts complicating their understanding and comparison. To address these issues, we propose a taxonomy of anonymization technologies, compare existing solutions, and develop a unifying framework that not only harmonizes concepts and terminology but also notation and nomenclature. We analyze the current state of the art and recent advances in the literature. The analysis enables us to understand the significance and appropriateness of the various proposals in achieving privacy., Javier Parra-Arnau is the recipient of a “Ramón y Cajal” fellowship (ref. RYC2021-034256-I) funded y the Spanish Ministry of Science and Innovation and the European Union– “NextGenerationEU”/PRTR (Plan de Recuperación, Transformación y Resiliencia). This work was also supported by the Spanish Government under the project “Enhancing Communication Protocols with Machine Learning while Protecting Sensitive Data (COMPROMISE)” PID2020-113795RB-C31, funded by MCIN/AEI/10.13039/501100011033, and through the project “MOBILYTICS” (TED2021-129782B-I00), funded by MCIN/AEI/10.13039/501100011033 and the European Union “NextGenerationEU”/PRTR., Postprint (author's final draft)

© 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works., Continuous data publishing consists in the republication of updating microdata. The most relevant syntactic notions in continuous data publishing are based on m-invariance. This notion enforces that no user can be distinguished among, at least, m-1 other users, each with distinct secret data. To achieve m-invariance, the existing methods must first alter the dataset to satisfy a property called m-eligibility. Essentially, a dataset can be made m-invariant if and only if it satisfies the m-eligibility constraint. Although guaranteeing the m-eligibility property is a crucial step, no theoretical study of the best strategies to achieve it has been carried out. This paper performs such a study by giving strategies and demonstrating their optimality under two approaches: insertion of counterfeit tuples and partial publication. The empirical evaluation of our proposal shows a significant reduction on the number of modifications needed to enforce m-eligbility of up to 41% with respect to the literature., Javier Parra-Arnau is the recipient of a “Ram´ on y Cajal” fellowship (ref. RYC2021-034256-I) funded by the Spanish Ministry of Science and Innovation and the European Union– “NextGenerationEU”/PRTR (Plan de Recuperaci´ on, Transformaci´ on y Resiliencia). This work was also supported by the Spanish Government under the project “Enhancing Communication Protocols with Machine Learning while Protecting Sensitive Data (COMPROMISE)” PID2020-113795RB-C31, funded by MCIN/AEI/10.13039/501100011033; the project “Anonymization technology for AI-based analytics of mobility data (MOBILYTICS)” (TED2021-129782B-I00), funded by MCIN/AEI/10.13039/501100011033 and the European Union“NextGenerationEU”/PRTR; andfundedbytheGeneralitat de Catalunya, under AGAUR grant “2021 SGR 01413”., Postprint (author's final draft)

Synthetic trajectory generation is crucial in order to study and analyse many fields, such as urban mobility, autonomous driving, mobile communication protocols, computational epidemiology and urban planning. Unfortunately, generating synthetic trajectory data is not an easy task, as it is a highly correlated, sequential type of data, with variable length. Currently, some work has been done on the field of synthetic trajectory generation, although none of them take into account the privacy of the user that has provided the trajectories. Furthermore, generating synthetic data without any security concern can put at risk users? privacy, since deep learning models that generate synthetic trajectories are susceptible to membership inference attacks and model inversion attacks. In this project we present an update of an existing deep learning model called MoveSim, which generates synthetic trajectory data. This model has been modified in order to achieve ?-Differential Privacy, and in this way guarantee privacy to the users that have provided their data without losing performance on the model.

Tesi amb menció de Doctorat Industrial, Tesi amb continguts retallats per motius de confidencialitat, (English) Online advertising makes possible most of the freely available online content and services. However, it is far from being mature and there are still controversial topics that need to be addressed. One of these topics is viewability, a metric that attempts to measure whether an impression could have been seen by a user. The purpose of this industrial doctorate, which was conducted within ExoClick ad-network, is to contribute to the improvement of the online advertising ecosystem through the study of viewability. To achieve this, we defined three main objectives: (1) to identify the viewability benefits and challenges, (2) to analyze the viewability standard and to test its possible implementations, and (3) to evaluate the impact of a viewability-driven feature in an ad company. For the first objective, we performed1. a systematic literature review of viewability. From this literature review, we identified the main benefits of viewability and the issues that should be addressed to promote its adoption. Then, to assess the second objective, we tested in ExoClick production environment a subset of viewability implementation methods to see their results on different dimensions. Afterward, for the third objective, we evaluated in different sites the impact of using viewability in ad-refresh, a technique used to replace the ad that has been already displayed on the publisher's site with a new ad. Finally, we discussed our results in terms of the three objectives of this dissertation, by providing an overview of the implications of viewability in the ad industry and the potential improvements of the current viewability standard, and by defining a set of guidelines to integrate it into any ad tech system. The results and conclusions obtained in this dissertation can motivate and help the different stakeholders to keep working on viewability adoption and integration to obtain a better, and more sustainable, online advertising ecosystem., (Español) La publicidad online hace posible que la mayor parte de los contenidos y servicios online sean gratuitos. Sin embargo, está lejos de considerase madura y hay temas controvertidos que deben abordarse. Uno de estos temas es el de la visibilidad, una métrica que intenta medir si una impresión ha podido ser vista por un usuario. El objetivo de este doctorado industrial, el cual se realiza en la empresa ExoClick, es contribuir a la mejora del ecosistema publicitario online mediante el estudio de la visibilidad. Para ello, hemos definido tres objetivos: (1) identificar los beneficios y desafíos de la visibilidad, (2) analizar el estandard actual y probar sus posibles implementaciones y (3) evaluar el impacto de una herramienta impulsada por la visibilidad dentro de una empresa publicitaria. Para el primer objetivo, hemos realizado una revisión bibliográfica sistemática de la visibilidad. A partir de esta revisión bibliográfica, hemos identificado los principales beneficios de la visibilidad y los problemas que deberían abordarse para promover su adopción. En relación al segundo objetivo, hemos probado en el entorno de producción de ExoClick un subconjunto de métodos de implementación de la visibilidad para ver sus resultados en diferentes dimensiones. Después, en relación al tercer objetivo, hemos evaluado el impacto de la visibilidad en “ad-refresh”, una técnica utilizada para remplazar el anuncio que se ha mostrado en la página web del publisher con un nuevo anuncio. Por último, hemos analizado nuestros resultados en función de los tres objetivos de esta tesis, proporcionando una visión general de las implicaciones de la visibilidad en la industria y de las posibles mejoras del actual estándar de visibilidad, y definiendo un conjunto de directrices para integrarla en cualquier sistema de tecnología publicitaria. Los resultados y conclusiones obtenidos en esta tesis pueden motivar y ayudar a seguir trabajando en la adopción e integración de la visibilidad para, Postprint (published version)

Col·lecció de problemes resolts d'IXT (Introducció a les Xarxes Telemàtiques) amb solució, 1er curs dels graus de l'ETSETB, 2022/2023, 2n quadrimestre

Col·lecció de problemes resolts d'IXT (Introducció a les Xarxes Telemàtiques) amb solució, 1er curs dels graus de l'ETSETB., 2022/2023, 2n quadrimestre

Trajectory analysis holds many promises, from improvements in traffic management to routing advice or infrastructure development. However, learning users’ paths is extremely privacy-invasive. Therefore, there is a necessity to protect trajectories such that we preserve the global properties, useful for analysis, while specific and private information of individuals remains inaccessible. Trajectories, however, are difficult to protect, since they are sequential, highly dimensional, correlated, bound to geophysical restrictions, and easily mapped to semantic points of interest. This paper aims to establish a systematic framework on protective masking measures for trajectory databases with differentially private (DP) guarantees, including also utility properties, derived from ideas and limitations of existing proposals. To reach this goal, we systematize the utility metrics used throughout the literature, deeply analyze the DP granularity notions, explore and elaborate on the state of the art on privacy-enhancing mechanisms and their problems, and expose the main limitations of DP notions in the context of trajectories., We would like to thank the reviewers and shepherd for their useful comments and suggestions in the improvement of this paper. Javier Parra-Arnau is the recipient of a “Ramón y Cajal” fellowship funded by the Spanish Ministry of Science and Innovation. This work also received support from “la Caixa” Foundation (fellowship code LCF/BQ/PR20/11770009), the European Union’s H2020 program (Marie Skłodowska-Curie grant agreement № 847648) from the Government of Spain under the project “COMPROMISE” (PID2020-113795RB-C31/AEI/10.13039/501100011033), and from the BMBF project “PROPOLIS” (16KIS1393K). The authors at KIT are supported by KASTEL Security Research Labs (Topic 46.23 of the Helmholtz Association) and Germany’s Excellence Strategy (EXC 2050/1 ‘CeTI’; ID 390696704)., Peer Reviewed, Postprint (published version)

This Master's thesis focuses on the increasing need for organizations to prioritize and maintain continuous security reviews for applications deployed in cloud environments. As more businesses embrace cloud computing, AWS has emerged as a leading provider offering a wide range of services. However, this growing reliance on AWS also increases the vulnerability to security threats. This thesis aims to highlight the potential risks associated with AWS deployments and emphasize the significance of conducting ongoing security reviews to ensure the integrity, confidentiality, and availability of applications and data. To accomplish this, the main objective of this project is to develop an automated tool that highlights potentially weak security configurations in AWS environments, which would help organizations be aware of the risks they are being exposed to.

Col·lecció de problemes resolts d'IXT (Introducció a les Xarxes Telemàtiques) amb solució, 1er curs dels graus de l'ETSETB., 2022/2023, 2n quadrimestre

Col·lecció de problemes resolts d'IXT (Introducció a les Xarxes Telemàtiques) amb solució, 1er curs dels graus de l'ETSETB., 2022/2023, 2n quadrimestre

Col·lecció de problemes resolts d'IXT (Introducció a les Xarxes Telemàtiques) amb solució, 1er curs dels graus de l'ETSETB., 2022/2023, 2n quadrimestre

La generación sintética de trayectorias es cru- cial para poder realizar estudios y análisis en diferentes campos, como la movilidad urbana, los protocolos de redes móviles, la epidemiología computacional o la simulación de cambios en la movilidad para la planificación urbana. Actual- mente, se ha observado que la generación de datos sintéticos sin protección adicional puede poner en riesgo la privacidad de los usuarios, dado que los modelos de aprendizaje profundo actuales son ataques de inferencia de membresía. un modelo de aprendizaje profundo ya existente llamado MoveSim, el cual genera datos sintéticos Este modelo ha sido modificado ferencialmente privado y, de esta manera, garantizar la privacidad de los usuarios que han proporcionado sus datos sin perder un rendimiento sustancial del modelo., Javier Parra-Arnau es el beneficiario de una beca Ramón y Cajal (ref. RYC2021-034256-1) financiada por el Ministerio de Ciencia e Innovación de España y la Unión Europea - "NextGenerationEU”/PRTR (Plan de Recuperación, Transformación y Resiliencia). Este trabajo también ha sido apoyado por el Gobierno español a través del proyecto “Enhancing Communication Protocols with Machine Learning while Protecting Sensitive Data (COMPROMISE” PID2020-113795RB-C31, financiado por MCIN/AEI/10.13039/501100011033, y a través del proyecto "MOBILYTICS” (TED2021-129782B-100), financiado por MCIN/AE1/10.13039/501100011033 y la Unión Europea "NextGenerationEU”/PRTR y financiado por la Generalitat de Catalunya mediante la beca AGAUR 72021 SGR 01413”., Peer Reviewed, Postprint (published version)

Postprint (published version)

Web tracking databases usually use pseudonyms to identify subjects. It has been proven that subjects web behaviour has enough information to identify them. In this thesis, we attempted to retrieve this identifying data through simple algorithms in an scenario representing a possible real case. The main questions were: how easy it was to link subjects to their click sessions using frequency analysis How many traces were needed to observe before successfully performing the attack. Three algorithms were tested: a histogram approach, taking into account domain frequencies; sequence alignment, checking sequence order; and Naive Bayes, calculating probabilities. We found that the histogram had a fine accuracy, but was surpassed by sequence alignment and Naive Bayes. Sequence alignment was too expensive computationally wise to use it in practice and Naive Bayes was the best in accuracy and good in execution time. The number of sessions required to observe for a user were around 30.

A variety of conditions and limiting properties complicate the anonymization of trajectory data, since they are sequential, high-dimensional, bound to geophysical restrictions and easily mapped to semantic points of interest and regions with known properties like suburban neighborhoods, industrial areas or city-centers. Learning the places where one has been is extremely privacy-invasive. However, analyzing real trajectories holds numerous promises, ranging from better informed traffic management, to location recommendations or computational social science, infrastructure and even urban development planning. The aim of this paper is to establish various challenges, stemming from ideas and also limitations of existing proposals for the anonymization of trajectories, and subsequently identify research opportunities. Keeping both utility and privacy challenges prominent, we sketch the way towards establishing a useful research framework and propose possible research venues towards privacy-preserving trajectory publication., Peer Reviewed, Postprint (author's final draft)

El analisis de las trayectorias encierra numerosas ´ promesas, desde mejoras en la gestion del tr ´ afico hasta recomen- ´ daciones de ruta, o incluso en el desarrollo de infraestructuras. Sin embargo, conocer los lugares en los que uno ha estado es extremadamente invasivo. Por ello, surge la necesidad de anonimizar bases de datos de trayectorias, preservando las estadísticas globales utiles para el an ´ alisis, mientras que la ínformacion espec ´ ífica y privada de los individuos permanece inaccesible. En este trabajo analizamos el estado del arte en la publicacion´ de trayectorias con garantías de privacidad, revisando nociones, mecanismos y metricas de utilidad. De este an ´ alisis concluimos ´ limitaciones de las propuestas actuales y teniendo en cuenta tanto los problemas de privacidad como los de utilidad, esbozamos oportunidades de investigacion para el desarrollo de mecanismos éficaces bajo una proteccion espec ´ ífica y rigurosa. Index Terms—privacidad de trayectorias, anonimizacion, no- ´ ciones sintacticas y sem ´ anticas, utilidad, privacidad diferencial., Este trabajo tam- ´ bien ha recibido el apoyo de la Fundaci ´ on “la Caixa” ´ (codigo de beca LCF/BQ/PR20/11770009), del programa ´ H2020 de la Union Europea (acuerdo de subvenci ´ on Marie ´ Skłodowska-Curie n.º 847648), del Gobierno de Espana en el ˜ marco del proyecto “COMPROMISE” (PID2020-113795RBC31/AEI/10.13039/501100011033), y del proyecto BMBF “PROPOLIS” (16KIS1393K). Los autores del KIT cuentan con el apoyo de KASTEL Security Research Labs (Tema 46.23 de la Asociacion Helmholtz) y de la Estrategia de ´ Excelencia de Alemania (EXC 2050/1 ‘CeTI’)., Peer Reviewed, Postprint (published version)

En esta investigación mostramos que una Red Adversarial Generativa de Wasserstein (WGAN) con un mecanismo de ruido de Privacidad Diferencial (DP) es capaz de generar datos de calidad preservando la privacidad de datos tabulares. Nos centramos en la generación de datos sobre movilidad sostenible y en los tipos de datos que se pueden esperar cuando se trata de movilidad sostenible. Probamos el DP-WGAN en dos conjuntos de datos de movilidad sostenible y lo evaluamos en términos de privacidad frente a utilidad. Implementamos un Ataque de Inferencia de Miembros (MIA) para evaluar la resistencia del modelo a los ataques que invaden la privacidad. Mostramos que la red WGAN es capaz de preservar la privacidad hasta un cierto nivel y que la adición de DP mejora la privacidad del modelo con pérdidas en la utilidad. Añadiendo DP al modelo somos capaces de obtener una resiliencia perfecta al MIA y pérdidas de utilidad del 10% como máximo, evaluadas en términos de un clasificador externo que distingue los datos reales de los generados., In this research we show that a Wasserstein Generative Adversarial Network (WGAN) with a Differential Privacy (DP) noise mechanism is able to generate quality data while preserving the privacy of tabular data. We focus on the generation of data about sustainable mobility and the data types one can expect when dealing with such data. We test the DP-WGAN on two sustainable mobility datasets and evaluate it in terms of privacy versus utility. We implement a Membership Inference Attack (MIA) to evaluate the resilience of the model on privacy invading attacks. We show that the WGAN network is able to preserve privacy up to a certain level and the addition of the DP improves the privacy of the model with losses on the utility. By adding DP to the model we are able to obtain perfect resilience to MIA and utility losses of 10% at most, evaluated in terms of an external classifier that distinguishes real from generated data.

© 2022 Elsevier. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0, Colombia government wants to implement electronic voting. However, the existing electronic voting protocols only include some of the required security features and Colombia needs a protocol with all these features to ensure fraud-free elections. In this paper, we present the design of SIVP (Secure Internet Voting Protocol), a new voting protocol for electoral processes, based on blind signatures and public key cryptography. This protocol has six phases and provides: eligibility, democracy, privacy, verifiability, accuracy, fairness, robustness, receipt-freeness and coercion-resistant. Also, we compare the number of cryptographic operations per phase of SIVP with other four protocols and conclude that the computational load of our protocol is not excessively high despite including more security features., Peer Reviewed, Postprint (author's final draft)

The SISCOM (Smart Services for Information Systems and Communication Networks) research group focuses on technologies that make it possible to provide intelligent services for information services and communication networks. We teach and do research in topics related to privacy, performance evaluation of networks, wireless adhoc and mesh networks, design of routing protocols, among others. Our research activities are funded by public research projects granted by the Spanish Government and the European Commission., Peer Reviewed, Postprint (published version)

© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works., Within the online advertising ecosystem, viewability is defined as the metric that measures if an ad impression had the chance of being viewable by a potential consumer. Although this metric has been presented as a potential game-changer within the ad industry, it has not been fully adopted by the stakeholders, mainly due to disagreement between the different parties on the standards to implement and measure it, and its potential benefits and drawbacks. In this study, we present a survey of the role that viewability can have on the main challenges of the online advertising ecosystem depicting the main applications, benefits and issues. With this objective, we provide an overall picture of how viewability can fit within the ecosystem, which can help the different stakeholders to work on its adoption, integration and establishing a research agenda., This work was supported by the Plan de Doctorados Industriales de la Secretaría de Universidades e Investigación del Departamento de Empresa y Conocimiento de la Generalitat de Catalunya under the Grant 2018-DI-059 and by ExoClick. Furthermore, this work received support from the Fellowship through ‘‘la Caixa’’ Foundation under Grant ID100010434, from the European Union’s Horizon 2020 Research and Innovation Program through Marie Skłodowska-Curie Grant under Agreement 847648, from the Fellowship under Grant CF/BQ/PR20/11770009, from the Spanish Ministry of Economy and Competitiveness through Juan de la Cierva Formación Program under Grant FJCI-2017-34926 and from the Spanish Government under Grant PID2020-113795RB-C31 ‘‘COMPROMISE’’., Peer Reviewed, Postprint (published version)

This research work is part of J. Estrada-Jiménez's Ph.D. thesis, The ability of the online marketing industry to track and pro le users' Web-browsing activity is what enables effective, tailored-made advertising services. The intrusiveness of these practices and the increasing invasiveness of digital advertising, however, have raised serious concerns regarding user privacy. Although the level of ubiquity of tracking and advertising has been investigated in top-world sites based in North America and Western Europe, the extent to which those practices are carried out in territories with less or no legal coverage in terms of data protection has not been studied so far. In this work, we present the rst detailed measurement of online tracking and advertising conducted to date in one of those regions, namely, Ibero-America, by analyzing local websites (e.g., education and government sites). In doing so, our measurement study aims to nd out how user location as well as the type of publisher may impact on tracking and advertising and thus user privacy. Lastly, our thorough, extensive analysis also explores whether differences are appreciated between Latin America and the EU with regard to the third-party tracking conducted from and towards the corresponding countries., This work was supported by the Spanish Ministerio de Economía y Competitividad (MINECO) through the project ``Secure SMArt Grid using Open Source Intelligence. Data Privacy and Reliable Communications (MAGOS)'', ref. TEC2017-84197-C4-3-R. The research was also supported by the La Caixa Foundation under Grant ID 100010434, in part by the European Union's Horizon 2020 Research and Innovation Programme through the Marie Sklodowska-Curie under Grant 847648, and in part by the Fellowship under Grant LCF/BQ/PR20/11770009., Peer Reviewed, Postprint (published version)

Major advances in information and communication technologies (ICTs) make citizens to be considered as sensors in motion. Carrying their mobile devices, moving in their connected vehicles or actively participating in social networks, citizens provide a wealth of information that, after properly processing, can support numerous applications for the benefit of the community. In the context of smart communities, the INRISCO [1] proposal intends for (i) the early detection of abnormal situations in cities (i.e., incidents), (ii) the analysis of whether, according to their impact, those incidents are really adverse for the community; and (iii) the automatic actuation by dissemination of appropriate information to citizens and authorities. Thus, INRISCO will identify and report on incidents in traffic (jam, accident) or public infrastructure (e.g., works, street cut), the occurrence of specific events that affect other citizens' life (e.g., demonstrations, concerts), or environmental problems (e.g., pollution, bad weather). It is of particular interest to this proposal the identification of incidents with a social and economic impact, which affects the quality of life of citizens., Peer Reviewed, Postprint (published version)

© <2020>. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0, Today’s countless benefits of exploiting data come with a hefty price in terms of privacy. -Anonymous microaggregation is a powerful technique devoted to revealing useful demographic information of microgroups of people, whilst protecting the privacy of individuals therein. Evidently, the inherent distortion of data results in the degradation of its utility. This work proposes and analyzes an anonymization method that draws upon the technique of linear discriminant analysis (LDA), with the aim of preserving the empirical utility of data. Further, this utility is measured as the accuracy of a machine learning model trained on the microaggregated data. By transforming the original data records to a different data space, LDA enables -anonymous microaggregation to build microcells more tailored to an intrinsic classification threshold. To do this, first, data is rotated (projected) towards the direction of maximum discrimination and, second, scaled in this direction by a factor that penalizes distortion across the classification threshold. The upshot is that thinner cells are built along the threshold, which ends up preserving data utility in terms of the accuracy of machine learned models for a number of standardized data sets., We gratefully acknowledge the invaluable assistance of Irene Carrión-Barberà, M.D., in the preparation of the medical example in Fig. 2. This work is supported by the Escuela Politécnica Nacional through the project ‘‘Privacidad Sintáctica Funcional: Análisis y adaptación de mecanismos de anonimato con enfoque en la preservación de utilidad de los datos’’, ref. PII-DETRI-2019-01. This work is partly supported by the Spanish Ministry of Economy and Competitiveness (MINECO) through the project ‘‘MAGOS’’, ref. TEC2017-84197-C4-3-R. Ana Rodríguez-Hoyos and José Estrada-Jiménez acknowledge the support from Escuela Politécnica Nacional (EPN), Republic of Ecuador for their doctoral studies at Universitat Politècnica de Catalunya (UPC)., Peer Reviewed, Postprint (author's final draft)

For some time now, big data applications have been enabling revolutionary innovation in every aspect of our daily life by taking advantage of lots of data generated from the interactions of users with technology. Supported by machine learning and unprecedented computation capabilities, different entities are capable of efficiently exploiting such data to obtain significant utility. However, since personal information is involved, these practices raise serious privacy concerns. Although multiple privacy protection mechanisms have been proposed, there are some challenges that need to be addressed for these mechanisms to be adopted in practice, i.e., to be “usable” beyond the privacy guarantee offered. To start, the real impact of privacy protection mechanisms on data utility is not clear, thus an empirical evaluation of such impact is crucial. Moreover, since privacy is commonly obtained through the perturbation of large data sets, usable privacy technologies may require not only preservation of data utility but also efficient algorithms in terms of computation speed. Satisfying both requirements is key to encourage the adoption of privacy initiatives. Although considerable effort has been devoted to design less “destructive” privacy mechanisms, the utility metrics employed may not be appropriate, thus the wellness of such mechanisms would be incorrectly measured. On the other hand, despite the advent of big data, more efficient approaches are not being considered. Not complying with the requirements of current applications may hinder the adoption of privacy technologies. In the first part of this thesis, we address the problem of measuring the effect of k-anonymous microaggregation on the empirical utility of microdata. We quantify utility accordingly as the accuracy of classification models learned from microaggregated data, evaluated over original test data. Our experiments show that the impact of the de facto microaggregation standard on the performance of machine, Les aplicacions de big data impulsen actualment una accelerada innovació aprofitant la gran quantitat d’informació generada a partir de les interaccions dels usuaris amb la tecnologia. Així, qualsevol entitat és capaç d'explotar eficientment les dades per obtenir utilitat, emprant aprenentatge automàtic i capacitats de còmput sense precedents. No obstant això, sorgeixen en aquest escenari serioses preocupacions pel que fa a la privacitat dels usuaris ja que hi ha informació personal involucrada. Tot i que s'han proposat diversos mecanismes de protecció, hi ha alguns reptes per a la seva adopció en la pràctica, és a dir perquè es puguin utilitzar. Per començar, l’impacte real d'aquests mecanismes en la utilitat de les dades no esta clar, raó per la qual la seva avaluació empírica és important. A més, considerant que actualment es manegen grans volums de dades, una privacitat usable requereix, no només preservació de la utilitat de les dades, sinó també algoritmes eficients en temes de temps de còmput. És clau satisfer tots dos requeriments per incentivar l’adopció de mesures de privacitat. Malgrat que hi ha diversos esforços per dissenyar mecanismes de privacitat menys "destructius", les mètriques d'utilitat emprades no serien apropiades, de manera que aquests mecanismes de protecció podrien estar sent incorrectament avaluats. D'altra banda, tot i l’adveniment del big data, la investigació existent no s’enfoca molt en millorar la seva eficiència. Lamentablement, si els requisits de les aplicacions actuals no es satisfan, s’obstaculitzarà l'adopció de tecnologies de privacitat. A la primera part d'aquesta tesi abordem el problema de mesurar l'impacte de la microagregació k-Gnónima en la utilitat empírica de microdades. Per això, quantifiquem la utilitat com la precisió de models de classificació obtinguts a partir de les dades microagregades. i avaluats sobre dades de prova originals. Els experiments mostren que l'impacte de l’algoritme de rmicroagregació estàndard en, Postprint (published version)

Many of the current online business base completely their revenue models in earnings from online advertisement. A problematic fact is that according to Google more than half of display ads are not being seen. The International Advertising Bureau (IAB) has defined a viewable impression as an impression that at least 50% of its pixels are rendered in the viewport during at least one continuous second. Although there is agreement on this definition for measuring viewable impressions in the industry, there is no systematic methodologies on how it should be implemented or the trustworthiness of these implementations. In fact, the Media Rating Council (MRC) announced that there are inconsistencies across multiple reports attempting to measure this metric. For this reason, we select a subset of implementations to track viewable impressions and we perform a case study by implementing them in a webpage registered in the worldwide ad-network ExoClick in order to see their results on different dimensions. Our results show that the Intersection Observer API is the implementation that detects more viewable impressions and that there are significant viewability differences depending on the banner location on the website. Finally, we also propose an ensemble viewability method that proves to be able to detect a higher number of viewable impressions., This work was possible thanks to the support of “Plan de Doctora-dos Industriales de la Secretaría de Universidades e Investigacióndel Departamento de Empresa y Conocimiento de la Generalitatde Catalunya” and the Spanish Ministry of Economy and Com-petitiveness through the Juan de la Cierva Formación program(FJCI-2017-34926). We also want to thank ExoClick for their sup-port in conducting this research., Peer Reviewed, Postprint (published version)

Many of the current online businesses base completely their revenue models in earnings from online advertisement. A problematic fact is that according to recent studies more than half of display ads are not being detected as viewable. The International Advertising Bureau (IAB) has defined a viewable impression as an impression that at least 50% of its pixels are rendered in the viewport during at least one continuous second. Although there is agreement on this definition for measuring viewable impressions in the industry, there is no systematic methodologies on how it should be implemented or the trustworthiness of these methods. In fact, the Media Rating Council (MRC) announced that there are inconsistencies across multiple reports attempting to measure this metric. In order to understand the magnitude of the problem, we conduct an analysis of different methods to track viewable impressions. Then, we test a subset of geometric and strong interaction methods in a webpage registered in the worldwide ad-network ExoClick, which currently serves over 7 billion geo-targeted ads a day to a global network of 65000 web/mobile publisher platforms. We find that the Intersection Observer API is the method that detects more viewable impressions given its robustness towards the technological constraints that face the rest of implementations available. The motivation of this work is to better understand the limitations and advantages of such methods, which can have an impact at a standardisation level in online advertising industry, as well as to provide guidelines for future research based on the lessons learned., This work was possible thanks to the support of “Plan de Doctorados Industriales de la Secretaría de Universidades e Investigación del Departamento de Empresa y Conocimiento de la Generalitat de Catalunya” and the Spanish Ministry of Economy and Competitiveness through the Juan de la Cierva Formación program (FJCI-2017-34926). We also want to thank ExoClick for their support in conducting thisresearch, Peer Reviewed, Postprint (published version)

© <2020>. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0, Identification and authentication (IA) are security procedures that are ubiquitous in our online life, and that constantly require disclosing personal, sensitive information to non-fully trusted service providers, or to fully trusted providers that unintentionally may fail to protect such information. Although user IA processes are extensively supported by heterogeneous software and hardware, the simultaneous protection of user privacy is an open problem. From a legal point of view, the European Union legislation requires protecting the processing of personal data and evaluating its impact on privacy throughout the whole IA procedure. Privacy Threat Analysis (PTA) is one of the pillars for the required Privacy Impact Assessment (PIA). Among the few existing approaches for conducting a PTA, LINDDUN is a very promising framework, although generic, in the sense that it has not been specifically conceived for IA. In this work, we investigate an extension of LINDDUN that allows performing a reliable and systematically-reproducible PTA of user IA processes, thereby contributing to one of the cornerstones of PIA. Specifically, we propose a high-level description of the IA verification process, which we illustrate with an UML use case. Then, we design an identification and authentication modelling framework, propose an extension of two critical steps of the LINDDUN scheme, and adapt and tailor the trust boundary concept applied in the original framework. Finally, we propose a systematic methodology aimed to help auditors apply the proposed improvements to the LINDDUN framework., The authors are thankful for the support through the research project “INRISCO”, ref. TEC2014-54335-C4-1-R, “MAGOS”, TEC2017-84197-C4-3-R, and the project “Sec-MCloud”, ref. TIN2016-80250-R. J. Parra-Arnau is the recipient of a Juan de la Cierva postdoctoral fellowship, IJCI-2016–28239, from the Spanish Ministry of Economy and Competitiveness. J. Parra-Arnau is with the UNESCO Chair in Data Privacy, but the views in this paper are his own and are not necessarily shared by UNESCO., Peer Reviewed, Postprint (author's final draft)

Since the development of telecommunication systems the secure transmission of sensible information has been one of the primary goals. Data confidentiality and data integrity are crucial when dealing with sensible information and for this reason techniques of secure storage and encryption, over time, grew in complexity and efficacy. Moreover, in modern times sensible files are often stored in different secure locations which do not have a secure link between them. This Master Thesis has as objective the creation of an encryption device able to safely store and share sensible information. This device enables users to securely read, transmit and receive sensible and encrypted files using different public key and disk encryption schemes. The result of this Master thesis is a device called TransCrypt which takes inspiration from the OpenPGP protocol to encrypt sensible files and allow them to securely travel to their recipient deploying several layers of disk encryption.

Online advertising is consistently considered as the pillar of the "free• content on the Web since it is commonly the funding source of websites. Furthermore, the option of delivering personalizad ads has tumed advertising into a really valuable service for users, who receive ads tailored to their interests. Given its success in getting paying customers, online advertising is fueling a billionaire business. The current advertising model builds upon an intricate infrastructure whose main aim is to deliver personalized ads. For this purpose, a wealth of user data is collected, aggregated, processed and traded at an unprecedented rate. However, the intrusiveness and ubiquity of these practices prorrpt serious privacy concems. In view of the inherent corrplexity behind the operation of ad platforms, privacy risks in the online advertising ecosystem could be studied from multiple perspectives. Naturally, most of the efforts unveiling these privacy issues concentrate on a specific entity, technology, behavior or context. However, such a segmented approach rright underestimate the benefits of a wider vision of a systerric problem. A lot of privacy protection echanisms have been proposed from the industry and acaderria. The most popular ones resort to radical strategies that hinder the ad distribution process, thus seriously affecting the online advertising ecosystem. Others involve significantly changing the ecosystem, which unfortunately may not be suitable in these times. Consequently, to encourage the adoption of privacy protection in this context, it is fundamental to pose mechanisms that aim at balancing the trade-off between user privacy and the web business model. First, this thesis deals with the need to have a wide perspective of the privacy risks for users within the online advertising ecosystem and the protection approaches available. We survey the online advertising infrastructure and its supporting technologies, and present a thorough overview of the undertyin, La publicitat en línia té un paper important a Internet que permet finançar habitualment l'operació de llocs web que ofereixen contingut lliure als usuaris. A més, la personalització dels anuncis ha tornat la publicitat en línia un servei valuós per als usuaris. Si aconseguirem que hi hagi molts compradors siguin més que possibles, es promourà un negoci milionari. El model d'anuncis vigents es basa en una infraestructura completa que lliura els anuncis personalitzats. Pera això, es pot recopilar una gran quantitat de dades d'ús, agregar, processar i vendre molt ràpidament. Malauradament, aquestes pràctiques generen riscos de privadesa. Donada la complexitat de l'operació de les plataformes d'anuncis, els riscos de privacitat es poden estudiar des de diverses perspectives. Naturalment, els esforços per desenvolupar aquests problemes de privacitat es concentren en una entitat, tecnologia, comportament o context específic. Però aquest enfocament subestima els beneficis d'una perspectiva més àmplia d'un problema integral. Molts mecanismes de protecció han estat proposats des de la indústria i l’àmbit acadèmic. Els més populars apliquen estratègies radicals que obstrueixen la distribució d'anuncis, afectant seriosament l’ecosistema d'anuncis. També es pot modificar significativament l’ecosistema, el que no és factible per la seva conflictivitat. Així, amb la finalitat de fomentar l'adopció de protecció de privacitat, és fonamental plantejar solucions orientades a equilibrar les necessitats de privacitat amb el model de negocis de la web. Inicialment, la tesi ofereix una visió amplia dels riscos de privacitat i els mecanismes de protecció a ecosistema d'anuncis en línia. Això es pot aconseguir basant-se en una revisió de la infraestructura i tecnologies subjacents en aquest context. Analitza sistemàticament les amenaces i potencies atacants. A continuació es revisa exhaustivament els mecanismes de privacitat més rellevants, i es classifica i es compara segons les garantie, Postprint (published version)

© <2019> Elsevier. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0, The massive exploitation of tons of data is currently guiding critical decisions in domains such as economics or health. But serious privacy risks arise since personal data is commonly involved. k-Anonymous microaggregation is a well-known method that guarantees individuals’ privacy while preserving much of data utility. Unfortunately, methods like this are computationally expensive in big data settings, whereas the application domain of data might require an immediate response to make “life or death” decisions. Accordingly, this paper proposes five strategies to simplify the internal operations (such as distance calculations and element sorting) of the maximum distance to average vector method, the de facto microaggregation standard. For the sake of its usability in large-scale databases, they, e.g., reduce the number of operations necessary to compute distances from 3m to 2m, where m is the number of attributes of the data set. Also, the complexity of sorting operations gets reduced from O(n log n) to O(n) where n is the number of records. Through extensive experimentation over multiple data sets, we show that the new algorithm gets significantly faster. Interestingly, the speedup factor by each technique is not greater than 2, but the multiplicative effect of combining them all turns the algorithm four times faster than the original microaggregation mechanism. This remarkable speedup factor is achieved, literally, with no additional cost in terms of data utility, i.e., it does not incur greater information loss., Peer Reviewed, Postprint (author's final draft)

© <2019> Elsevier. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0, The technical contents of this work fall within the statistical disclosure control (SDC) field, which concerns the postprocessing of the demographic portion of the statistical results of surveys containing sensitive personal information, in order to effectively safeguard the anonymity of the participating respondents. A widely known technique to solve the problem of protecting the privacy of the respondents involved beyond the mere suppression of their identifiers is the k-anonymous microaggregation. Unfortunately, most microaggregation algorithms that produce competitively low levels of distortions exhibit a superlinear running time, typically scaling with the square of the number of records in the dataset. This work proposes and analyzes an optimized prepartitioning strategy to reduce significantly the running time for the k-anonymous microaggregation algorithm operating on large datasets, with mild loss in data utility with respect to that of MDAV, the underlying method. The optimization strategy is based on prepartitioning a dataset recursively until the desired k-anonymity parameter is achieved. Traditional microaggregation algorithms have quadratic computational complexity in the form T(n2). By using the proposed method and fixing the number of recurrent prepartitions we obtain subquadratic complexity in the form T(n3/2), T(n4/3), ..., depending on the number of prepartitions. Alternatively, fixing the ratio between the size of the microcell and the macrocell on each prepartition, quasilinear complexity in the form T(nlog¿n) is achieved. Our method is readily applicable to large-scale datasets with numerical demographic attributes., Peer Reviewed, Postprint (author's final draft)

Despite its undeniable advantages, the exponential growth of data analytic capabilities implies a significant increase in the risk of personal privacy loss and re-identification of the individuals appearing in databases. A tool capable of treating the data in a way that would avoid re-identification of the user and, thus, could be shared to other companies or to study groups would be undoubtedly effective. The main objective of this project focuses on anonymization of textual data. That is, the goal is to create a tool that enables the anonymization of an input text to prevent identity disclosure while conserving as much utility as possible. This is achieved with the help of Information Theory and Natural Language Processing techniques. Additionally, the user interaction is required to make certain decisions, having as a consequence a semi-automatic anonymization tool for textual data. Because of that, we have developed a Graphical User Interface. The evaluation of the tool in automatic mode has been carried out in order to check its performance in function of certain parameters., A pesar de sus innegables ventajas, el crecimiento exponencial de las capacidades de análisis de datos han comportado un crecimiento significativo del riesgo de pérdida de privacidad del individuo. Una herramienta capaz de tratar los datos de forma que se evitara la re-identificación del usuario y, por lo tanto, permitiera que los datos pudiesen ser compartidos a empresas o grupos de estudio, sería indudablemente útil. El objetivo principal de este proyecto se basa en la anonimización de documentos textuales. Así, el objetivo es la creación de una herramienta que permita la anonimización de un texto de entrada para prevenir la re-identificación del usuario y, a la vez, conserve tanta utilidad del documento como sea posible. Esto es posible con la ayuda de la Teoría de la Información y técnicas de NLP (Natural Language Processing). Además, la interacción del usuario es necesaria para la toma de ciertas decisiones, obteniendo en consecuencia una herramienta semi-automática para anonimización de documentos textuales. Es por eso que se ha desarrollado una Interfaz Gráfica de Usuario. Se ha llevado a cabo la evaluación de la herramienta para analizar su comportamiento en función de varios parámetros., Malgrat els innegables avantatges que aporta, el creixement exponencial de les capacitats d'anàlisi de dades comporta un creixement significatiu del risc de pèrdua de privacitat i re-identificació d'individus que apareixen en bases de dades. Una eina capaç de tractar les dades de manera que s'impedeixi la re-identificació de l'usuari i, per tant, permeti compartir les dades per estudis o a altres empreses seria, sens dubte, útil. L'objectiu principal d'aquest projecte es basa en l'anonimització de documents textuals. D'aquesta manera, l'objectiu és crear una eina que permeti l'anonimització d'un document textual d'entrada per prevenir que es reveli la seva identitat procurant tant com sigui possible mantenir la utilitat del document. Això és possible amb l'ajuda de la Teoria de l'Informació i de tècniques de NLP (Natural Language Processing). A més, la interacció de l'usuari es requereix per prendre certes decisions, obtenint, com a conseqüència, una eina semi-automàtica per l'anonimització de documents textuals. És per aquest motiu que s'ha desenvolupat una interfície gràfica d'usuari. A més, s'ha dut a terme l'evaluació de l'eina en mode automàtic per analitzar el seu comportament en funció de certs paràmetres.

In the recent years, the majority of the world’s Critical Infrastructures (CIs) have evolved to be more flexible, cost efficient and able to offer better services and conditions for business growth. Through this evolution, CIs and companies offering CI services had to adopt many of the recent advances CIPSEC-Enhancing Critical Infrastructure Protection of the Information and Communication Technologies (ICT) field. This rapid adaptation however, was performed without thorough evaluation of its impact on CIs’ security. It resulted into leaving CIs vulnerable to a new set of threats and vulnerabilities that impose high levels of risk to the public safety, economy and welfare of the population. To this extend, the main approach for protecting CIs includes handling them as comprehensive entities and offer a complete solution for their overall infrastructures and ICT systems (IT&OT departments). However, complete CI security solutions exist, in the form of individual products from IT security companies. These products, integrate only in-house designed and developed tools/solutions, thus offering a limited range of technical solutions. The main aim of CIPSEC is to create a unified security framework that orchestrates state-of-the-art heterogeneous security products to offer high levels of protection in IT (information technology) and OT (operational technology) departments of CIs, also offering a complete security ecosystem of additional services. These services include vulnerability tests and recommendations, key personnel training courses, public-private partnerships (PPPs), forensics analysis, standardization activities and analysis against cascading effects., Peer Reviewed, Postprint (published version)

© <2019>. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0, k-Anonymous microaggregation is a widespread technique to address the problem of protecting the privacy of the respondents involved beyond the mere suppression of their identifiers, in applications where preserving the utility of the information disclosed is critical. Unfortunately, microaggregation methods with high data utility may impose stringent computational demands when dealing with datasets containing a large number of records and attributes. This work proposes and analyzes various anonymization methods which draw upon the algebraic-statistical technique of principal component analysis (PCA), in order to effectively reduce the number of attributes processed, that is, the dimension of the multivariate microaggregation problem at hand. By preserving to a high degree the energy of the numerical dataset and carefully choosing the number of dominant components to process, we manage to achieve remarkable reductions in running time and memory usage with negligible impact in information utility. Our methods are readily applicable to high-utility SDC of large-scale datasets with numerical demographic attributes., Peer Reviewed, Postprint (author's final draft)

Online tracking is the key enabling technology of modern online advertising. In the recently established model of real-time bidding (RTB), the web pages tracked by ad platforms are shared with advertising agencies (also called DSPs), which, in an auction-based system, may bid for user ad impressions. Since tracking data are no longer confined to ad platforms, RTB poses serious risks to privacy, especially with regard to user profiling, a practice that can be conducted at a very low cost by any DSP or related agency, as we reveal here. In this work, we illustrate these privacy risks by examining a data set with the real ad-auctions of a DSP, and show that for at least 55% of the users tracked by this agency, it paid nothing for their browsing data. To mitigate this abuse, we propose a system that regulates the distribution of bid requests (containing user tracking data) to potentially interested bidders, depending on their previous behavior. In our approach, an ad platform restricts the sharing of tracking data by limiting the number of DSPs participating in each auction, thereby leaving unchanged the current RTB architecture and protocols. However, doing so may have an evident impact on the ad platform’s revenue. The proposed system is designed accordingly, to ensure the revenue is maximized while the abuse by DSPs is prevented to a large degree. Experimental results seem to suggest that our system is able to correct misbehaving DSPs, and consequently enhance user privacy., Peer Reviewed, Postprint (author's final draft)

With the activation of the latest GDPR legislation, companies are restricted in the use of the user data which has been collected. This data was able to be process in many ways without the user?s permission, but now the user?s permission is needed, despite there still being aspects in which the data is not allowed to be used. The project set out to create an environment which will aid companies overcome this problem by making the collected data impersonal and therefore, comply with the GDPR. This has been accomplished by creating The ANT, an Environment that uses policies to help sanitise large databases containing personal data. These policies are created by the companies, by defining a series of regular expressions, thereby providing them with the flexibility and freedom to sanitise this data according to their needs. This process will help companies continue using the data, as well as helping social media platforms users maintain their safety from any data leaks., Con la activación de la última legislación llamada GDPR, las empresas han sido restringidas en el uso de los datos de usuario que se habían recopilado. Estos datos se podían procesar de muchas maneras sin el permiso del usuario, pero ahora se necesita el permiso del usuario, a pesar de que todavía hay aspectos en los que no se permite el uso de los datos. El proyecto se propuso para crear un entorno que ayude a las empresas a superar este problema haciendo que los datos recopilados sean impersonales y, por lo tanto, cumplan con el GDPR. Esto se ha logrado mediante la creación de The ANT, un entorno que utiliza políticas para ayudar a sanear grandes bases de datos que contienen datos personales. Estas políticas son creadas por las compañías, al definir una serie de expresiones regulares, lo que les conlleva la flexibilidad y la libertad para sanear estos datos de acuerdo con sus necesidades. Este proceso ayudará a las empresas a seguir usando los datos, así como a los usuarios de las plataformas de redes sociales a mantener su seguridad ante cualquier pérdida de datos., Amb l'activació de l'última legislació anomenada GDPR, a les empreses s'ha restringit l'ús de les dades d'usuari que s'hagin recollit. Aquestes dades es podien processar de moltes maneres sense el permís de l'usuari, però ara es necessita el permís de l'usuari, tot i que encara hi ha aspectes en què no es permet la utilització de dades. El projecte s'ha proposat per crear un entorn que ajudi a les empreses a superar aquest problema fent que les dades recollides siguin impersonals i, per tant, compleixin amb el GDPR. Això s'ha aconseguit mitjançant la creació de The ANT, un entorn que utilitza polítiques per ajudar a sanejar grans bases de dades que contenen dades personals. Aquestes polítiques són creades per les empreses, definint una sèrie d'expressions regulars, oferint així la flexibilitat i la llibertat per a sanejar aquestes dades segons les seves necessitats. Aquest procés ajudarà a les empreses a continuar utilitzant les dades, així com a ajudar els usuaris de les plataformes de xarxes socials a mantenir la seguretat davant qualsevol filtració de dades.

This is a post-peer-review, pre-copyedit version of an article published in Eurasip journal on wireless communication and networking. The final authenticated version is available online at: http://dx.doi.org/10.1186/s13638-019-1375-7, Routing in mobile ad hoc networks is based on the cooperation of the network’s nodes. The presence of selfish nodes that do not cooperate in this task drastically reduces the number of delivered packets. In order to find the better paths that include nodes willing to cooperate, we propose a new routing algorithm based on the reputation of the nodes. In our proposal, each node locally assigns a reputation value to the rest of the nodes in the network and next it uses the assigned reputation values to find out the better routing paths, in order to minimize the overall packet loss ratio. We assume that nodes have a stationary routing behavior, but we also include a mechanism to detect changes in their behavior. Our approach has been evaluated in the presence of selfish nodes, in order to compare it with the dynamic source routing algorithm, obtaining a reduction in the packet loss ratio at the expenses of a small increase in the number of hops taken by the packets to reach their destinations., This work was supported in part by the Spanish Government under projects INRISCO (TEC2014–-54335-C4-1-R) and MAGOS (TEC2017-84197-C4-3-R)., Peer Reviewed, Postprint (published version)

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works., Online tracking has become a great enabler of massive surveillance so it is now a critical vector for threatening the privacy of users. Despite the benefits of online tracking for personalized advertising, the complexity of the involved platforms makes it a threat for democracy. In this work, online tracking is measured in Ecuador, a country with a developing adoption of online advertising technologies, having the highest Internet penetration rate in Latin America, but lacking regulation for privacy. By finding out the third party connections triggered through the most popular Ecuadorian websites, the concentration of online tracking is measured in Ecuador. Its impact is also analyzed by studying some particularities in government websites, the usage of advanced mechanisms of tracking, and the adoption of transparency practices in advertising platforms. Our final aim is exposing potential privacy violations., This work was partly supported by the Spanish Ministry ofEconomy and Competitiveness (MINECO) through the project“MAGOS”, ref. TEC2017-84197-C4-3-R. J. Parra-Arnau wassupported by the Spanish government under grant TIN2016-80250-R and by the Catalan government under grant 2017SGR 00705 and is currently the recipient of a Juan de la Ciervapostdoctoral fellowship, IJCI-2016-28239, from the SpanishMinistry of Economy and Competitiveness., Peer Reviewed, Postprint (author's final draft)

Cybersecurity logs are permanently generated by network devices to describe security incidents. With modern computing technology, such logs can be exploited to counter threats in real time or before they gain a foothold. To improve these capabilities, logs are usually shared with external entities. However, since cybersecurity logs might contain sensitive data, serious privacy concerns arise, even more when critical infrastructures (CI), handling strategic data, are involved. We propose a tool to protect privacy by anonymizing sensitive data included in cybersecurity logs. We implement anonymization mechanisms grouped through the definition of a privacy policy. We adapt said approach to the context of the EU project CIPSEC that builds a unified security framework to orchestrate security products, thus offering better protection to a group of CIs. Since this framework collects and processes security-related data from multiple devices of CIs, our work is devoted to protecting privacy by integrating our anonymization approach., Peer Reviewed, Postprint (published version)

The project arose thanks to the participation of the Universitat Politècnica de Catalunya in the European project H2020, called CIPSEC. Its collaboration consists on the creation of a tool able to anonymize data picked up from cyber-attacks targeting different enterprises for its further analyisis. This work consists on the creation of a tool which allows the sanitation of files for its further use being able to ensure the security of the information. Finally, this tool is a little platform which allows to perform a personalized sanitation., El proyecto surgió gracias a la participación por parte de la Universitat Politècnica de Catalunya en el proyecto europeo H2020 denominado CIPSEC. Su colaboración consiste en la creación de una herramienta que anonimice los datos recolectados de los ciberataques registrados en distintas compañías para su posterior uso en investigaciones. Este trabajo consiste en la creación de una herramienta que permite la sanitización de ficheros para su posterior uso pudiendo asegurar la seguridad en la información. Finalmente, esta herramienta es una pequeña plataforma con varios programas que permiten llevar a cabo una sanitización personalizada., El projecte va sorgir gracies a la participació per part de la Universitat Politècnica de Catalunya en el projecte europeu H2020 denominat CIPSEC. La seva col·laboració consisteix en la creació de una eina que anonimitzi las dades recol·lectades dels ciberatacs registrats en distintes companyies per el seu posterior ús en investigacions. Aquest treball consisteix en la creació d'una eina que permeti la sanitització de fitxers per el seu posterior ús podent assegurar la seguretat en la informació. Finalment aquesta eina és una petita plataforma que permet dur a terme una sanitització personalitzada.

Improvements in technology have led to enormous volumes of detailed personal information made available for any number of statistical studies. This has stimulated the need for anonymization techniques striving to attain a difficult compromise between the usefulness of the data and the protection of our privacy. k-Anonymous microaggregation permits releasing a dataset where each person remains indistinguishable from other k–1 individuals, through the aggregation of demographic attributes, otherwise a potential culprit for respondent reidentification. Although privacy guarantees are by no means absolute, the elegant simplicity of the k-anonymity criterion and the excellent preservation of information utility of microaggregation algorithms has turned them into widely popular approaches whenever data utility is critical. Unfortunately, high-utility algorithms on large datasets inherently require extensive computation. This work addresses the need of running k-anonymous microaggregation efficiently with mild distortion loss, exploiting the fact that the data may arrive over an extended period of time. Specifically, we propose to split the original dataset into two portions that will be processed subsequently, allowing the first process to start before the entire dataset is received, while leveraging the superlinearity of the microaggregation algorithms involved. A detailed mathematical formulation enables us to calculate the optimal time for the fastest anonymization, as well as for minimum distortion under a given deadline. Two incremental microaggregation algorithms are devised, for which extensive experimentation is reported. The theoretical methodology presented should prove invaluable in numerous data-collection applications, including largescale electronic surveys in which computation is possible as the data comes in., Peer Reviewed, Postprint (published version)

n the era of big data, the availability of massive amounts of information makes privacy protection more necessary than ever. Among a variety of anonymization mechanisms, microaggregation is a common approach to satisfy the popular requirement of k-anonymity in statistical databases. In essence, k-anonymous microaggregation aggregates quasi-identifiers to hide the identity of each data subject within a group of other k - 1 subjects. As any perturbative mechanism, however, anonymization comes at the cost of some information loss that may hinder the ulterior purpose of the released data, which very often is building machine-learning models for macrotrends analysis. To assess the impact of microaggregation on the utility of the anonymized data, it is necessary to evaluate the resulting accuracy of said models. In this paper, we address the problem of measuring the effect of k-anonymous microaggregation on the empirical utility of microdata. We quantify utility accordingly as the accuracy of classification models learned from microaggregated data, and evaluated over original test data. Our experiments indicate, with some consistency, that the impact of the de facto microaggregation standard (maximum distance to average vector) on the performance of machine-learning algorithms is often minor to negligible for a wide range of k for a variety of classification algorithms and data sets. Furthermore, experimental evidences suggest that the traditional measure of distortion in the community of microdata anonymization may be inappropriate for evaluating the utility of microaggregated data., Postprint (published version)