Knowing Your Population: Privacy-Sensitive Mining of Massive Data

Location and mobility patterns of individuals are important to environmental planning, societal resilience, public health, and a host of commercial applications. Mining telecommunication traffic and transactions data for such purposes is controversial, in particular raising issues of privacy. However, our hypothesis is that privacy-sensitive uses are possible and often beneficial enough to warrant considerable research and development efforts. Our work contends that peoples behavior can yield patterns of both significant commercial, and research, value. For such purposes, methods and algorithms for mining telecommunication data to extract commonly used routes and locations, articulated through time-geographical constructs, are described in a case study within the area of transportation planning and analysis. From the outset, these were designed to balance the privacy of subscribers and the added value of mobility patterns derived from their mobile communication traffic and transactions data. Our work directly contrasts the current, commonly held notion that value can only be added to services by directly monitoring the behavior of individuals, such as in current attempts at location-based services. We position our work within relevant legal frameworks for privacy and data protection, and show that our methods comply with such requirements and also follow best-practice

From Social Data Mining to Forecasting Socio-Economic Crisis

Socio-economic data mining has a great potential in terms of gaining a better understanding of problems that our economy and society are facing, such as financial instability, shortages of resources, or conflicts. Without large-scale data mining, progress in these areas seems hard or impossible. Therefore, a suitable, distributed data mining infrastructure and research centers should be built in Europe. It also appears appropriate to build a network of Crisis Observatories. They can be imagined as laboratories devoted to the gathering and processing of enormous volumes of data on both natural systems such as the Earth and its ecosystem, as well as on human techno-socio-economic systems, so as to gain early warnings of impending events. Reality mining provides the chance to adapt more quickly and more accurately to changing situations. Further opportunities arise by individually customized services, which however should be provided in a privacy-respecting way. This requires the development of novel ICT (such as a self- organizing Web), but most likely new legal regulations and suitable institutions as well. As long as such regulations are lacking on a world-wide scale, it is in the public interest that scientists explore what can be done with the huge data available. Big data do have the potential to change or even threaten democratic societies. The same applies to sudden and large-scale failures of ICT systems. Therefore, dealing with data must be done with a large degree of responsibility and care. Self-interests of individuals, companies or institutions have limits, where the public interest is affected, and public interest is not a sufficient justification to violate human rights of individuals. Privacy is a high good, as confidentiality is, and damaging it would have serious side effects for society.Comment: 65 pages, 1 figure, Visioneer White Paper, see http://www.visioneer.ethz.c

Online advertising: analysis of privacy threats and protection approaches

Online advertising, the pillar of the “free” content on the Web, has revolutionized the marketing business in recent years by creating a myriad of new opportunities for advertisers to reach potential customers. The current advertising model builds upon an intricate infrastructure composed of a variety of intermediary entities and technologies whose main aim is to deliver personalized ads. For this purpose, a wealth of user data is collected, aggregated, processed and traded behind the scenes at an unprecedented rate. Despite the enormous value of online advertising, however, the intrusiveness and ubiquity of these practices prompt serious privacy concerns. This article surveys the online advertising infrastructure and its supporting technologies, and presents a thorough overview of the underlying privacy risks and the solutions that may mitigate them. We first analyze the threats and potential privacy attackers in this scenario of online advertising. In particular, we examine the main components of the advertising infrastructure in terms of tracking capabilities, data collection, aggregation level and privacy risk, and overview the tracking and data-sharing technologies employed by these components. Then, we conduct a comprehensive survey of the most relevant privacy mechanisms, and classify and compare them on the basis of their privacy guarantees and impact on the Web.Peer ReviewedPostprint (author's final draft

FABRIC: A National-Scale Programmable Experimental Network Infrastructure

FABRIC is a unique national research infrastructure to enable cutting-edge and exploratory research at-scale in networking, cybersecurity, distributed computing and storage systems, machine learning, and science applications. It is an everywhere-programmable nationwide instrument comprised of novel extensible network elements equipped with large amounts of compute and storage, interconnected by high speed, dedicated optical links. It will connect a number of specialized testbeds for cloud research (NSF Cloud testbeds CloudLab and Chameleon), for research beyond 5G technologies (Platforms for Advanced Wireless Research or PAWR), as well as production high-performance computing facilities and science instruments to create a rich fabric for a wide variety of experimental activities

Privacy in online advertising platforms

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 undertying privacy risks and the solutions that may rritigate them. Through a systematic effort, we analyze the threats and potential privacy attackers in this scenario of online advertising.Then, we conduct a corrprehensive survey of the most relevant privacy mechanisms, and classify and con-pare them on the basis of their privacy guarantees and irrpact on the Web. Subsequently, we study the privacy risks derived from real-time bidding, a key enabling technology of modem online advertising. We experimentally explore the potential abuse of the process of user data sharing, necessary to support the auction-based system in online advertising. Accordingly, we propase a system to regula te the distribution of u ser tracking data to potentially interested entities, depending on their previous behavior.This consists in reducing the nurnber of advertising agencies receiving user data. Doing so may affect the ad platform's revenue, thus the proposed system is designed to maxirrize the revenue while the abuse by advertising agencies is prevented to a large degree. Experimentally, the results of evaluation suggest that this system is able to correct rrisbehaving entities, consequently enhancing user privacy. Finally, we analyze the irrpact of online advertising and tracking from the particular perspective of lberoamerica.We study the third-party and ad tracking triggered within local websites in this heterogeneous region not previously studied. We found out that user location in this context would affect privacy since the intensity of third-party traffic, including advertising related flows of information, varies from country to country when local web traffic is simulated, although the total nurnber of entities behind this traffic seems stable. The type of content served by websites is also a parameter affecting the leve! of third-party tracking:publishers assiciated with news shopping categories generate more third-party traffic and such intensity is exarbated for top-world sitesLa 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 garanties de privacitat que s'ofereixen i el seu possible impacte a la web. Seguidament, s'estudia els riscos de privadesa derivats de les ofertes en temps real, una tecnologia clau del sistema d'anuncis en línia modern. Experimentalment, s'inverteixen els riscos del procés de distribució de dades d'ús, part del sistema basat en licitacions de la publicitat en línia. Es proposa un sistema que regula la distribució de dades d'ús a tercers, depenent del seu comportament previ. Això consisteix en reduir el nombre d’agències anunciants que rebin dades d'ús. Per mitigar l’impacte sobre els ingressos del sistema d'anuncis, aquesta reducció és malaltia i l'objectiu de maximitzar els declaracions ingressades. Experimentalment, es troba que el sistema proposat corregir els comportaments maliciosos, millorant la privacitat dels usuaris. Finalment, s'analitza l'impacte del rastre i la publicitat en línia des de la perspectiva iberoamericana. Estudiem el rastreig de tercers i allò relacionat amb els anuncis que se generen en llocs web locals en aquesta regió heterogènia. Trobem que la ubicació de l'usuari en aquest context afecta la privacitat de l'usuari ja que aquest rastreig varia de país a país, tot i que el nombre total d'entitats darrere d'aquest transit sembla estable. El tipus de contingut afecta també el nivell de rastreig: llocs web de noticies o de compres generen més transit cap a tercers i aquesta intensitat s'exacerba en els llocs més populars

Privacy in online advertising platforms

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 undertying privacy risks and the solutions that may rritigate them. Through a systematic effort, we analyze the threats and potential privacy attackers in this scenario of online advertising.Then, we conduct a corrprehensive survey of the most relevant privacy mechanisms, and classify and con-pare them on the basis of their privacy guarantees and irrpact on the Web. Subsequently, we study the privacy risks derived from real-time bidding, a key enabling technology of modem online advertising. We experimentally explore the potential abuse of the process of user data sharing, necessary to support the auction-based system in online advertising. Accordingly, we propase a system to regula te the distribution of u ser tracking data to potentially interested entities, depending on their previous behavior.This consists in reducing the nurnber of advertising agencies receiving user data. Doing so may affect the ad platform's revenue, thus the proposed system is designed to maxirrize the revenue while the abuse by advertising agencies is prevented to a large degree. Experimentally, the results of evaluation suggest that this system is able to correct rrisbehaving entities, consequently enhancing user privacy. Finally, we analyze the irrpact of online advertising and tracking from the particular perspective of lberoamerica.We study the third-party and ad tracking triggered within local websites in this heterogeneous region not previously studied. We found out that user location in this context would affect privacy since the intensity of third-party traffic, including advertising related flows of information, varies from country to country when local web traffic is simulated, although the total nurnber of entities behind this traffic seems stable. The type of content served by websites is also a parameter affecting the leve! of third-party tracking:publishers assiciated with news shopping categories generate more third-party traffic and such intensity is exarbated for top-world sitesLa 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 garanties de privacitat que s'ofereixen i el seu possible impacte a la web. Seguidament, s'estudia els riscos de privadesa derivats de les ofertes en temps real, una tecnologia clau del sistema d'anuncis en línia modern. Experimentalment, s'inverteixen els riscos del procés de distribució de dades d'ús, part del sistema basat en licitacions de la publicitat en línia. Es proposa un sistema que regula la distribució de dades d'ús a tercers, depenent del seu comportament previ. Això consisteix en reduir el nombre d’agències anunciants que rebin dades d'ús. Per mitigar l’impacte sobre els ingressos del sistema d'anuncis, aquesta reducció és malaltia i l'objectiu de maximitzar els declaracions ingressades. Experimentalment, es troba que el sistema proposat corregir els comportaments maliciosos, millorant la privacitat dels usuaris. Finalment, s'analitza l'impacte del rastre i la publicitat en línia des de la perspectiva iberoamericana. Estudiem el rastreig de tercers i allò relacionat amb els anuncis que se generen en llocs web locals en aquesta regió heterogènia. Trobem que la ubicació de l'usuari en aquest context afecta la privacitat de l'usuari ja que aquest rastreig varia de país a país, tot i que el nombre total d'entitats darrere d'aquest transit sembla estable. El tipus de contingut afecta també el nivell de rastreig: llocs web de noticies o de compres generen més transit cap a tercers i aquesta intensitat s'exacerba en els llocs més populars.Postprint (published version