Publication: Design, analysis and implementation of advanced methodologies to measure the socio-economic impact of personal data in large online services
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2021-09
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2021-10-07
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Abstract
El ecosistema web es enorme y, en general, se sustenta principalmente en un atributo
intangible que sostiene la mayoría de los servicios gratuitos: la explotación de la información
personal del usuario. A lo largo de los años, la preocupación por la forma en que los
servicios utilizan los datos personales ha aumentado y atraído la atención de los medios de
comunicación, gobiernos, reguladores y también de los usuarios. Esta recogida de información
personal es hoy en día la principal fuente de ingresos en Internet. Además, por si fuera poco,
la publicidad online es la pieza que lo sustenta todo. Sin la existencia de datos personales
en comunión con la publicidad online, Internet probablemente no sería el gigante que hoy
conocemos.
La publicidad online es un ecosistema muy complejo en el que participan múltiples actores.
Es el motor principal que genera ingresos en la red, y en pocos años ha evolucionado hasta
llegar a miles de millones de usuarios en todo el mundo. Mientras navegan, los usuarios
generan datos muy valiosos sobre sí mismos que los anunciantes utilizan después para ofrecerles
productos relevantes en los que podrían estar interesados. Se trata de un enfoque bidireccional,
ya que los anunciantes pagan a intermediarios para que muestren anuncios al público que,
en principio, está más interesado. Sin embargo, este comercio, intercambio y tratamiento
de datos personales, además de abrir nuevas vías de publicidad, exponen la privacidad de
los usuarios. Esta incesante recopilación y comercialización de la información personal suele
quedar tras un muro opaco, donde el usuario generalmente desconoce para qué se utilizan sus
datos.
Las iniciativas de privacidad y transparencia se han incrementado a lo largo de los años
para empoderar al usuario en este negocio que mueve miles de millones de dólares en ingresos.
No en vano, tras varios escándalos, como el de Facebook Cambridge Analytica, las empresas
y los reguladores se han unido para crear transparencia y proteger a los usuarios de las malas
prácticas derivadas del uso de su información personal. Por ejemplo, el Reglamento General
de Protección de Datos, es el ejemplo más prometedor de regulación, que afecta a todos
los estados miembros de la Unión Europea, abogando por la protección de los usuarios. El
contenido de esta tesis tomará como referencia esta legislación.
Por todo ello, el propósito de esta tesis consiste en aportar herramientas y metodologías
que pongan de manifiesto usos inapropiados de datos personales por las grandes compañías
del ecosistema publicitario online, y cree transparencia entre los usuarios, proporcionando, a su vez, soluciones para que se protejan. Así pues, el contenido de esta tesis ofrece diseño,
análisis e implementación de metodologías que miden el impacto social y económico de la
información personal online en los servicios extensivos de Internet. Principalmente, se centra
en Facebook, una de las mayores redes sociales y servicios en la web, que cuenta con más de
2,8B de usuarios en todo el mundo y generó unos ingresos solo en publicidad online de más
de 84 mil millones de dólares en el año 2020.
En primer lugar, esta tesis presenta una solución, en forma de extensión del navegador
llamada FDVT (Data Valuation Tool for Facebook users), para proporcionar a los usuarios
una estimación personalizada y en tiempo real del dinero que están generando para Facebook.
Analizando el número de anuncios e interacciones en una sesión, el usuario obtiene información
sobre su valor dentro de esta red social. La extensión del navegador ha tenido una importante
repercusión y adopción tanto por parte de los usuarios, instalándose más de 10k veces desde
su lanzamiento público en octubre de 2016, como de los medios de comunicación, apareciendo
en más de 100 medios.
En segundo lugar, el estudio e investigación de los posibles riesgos asociados al tratamiento
de los datos de los usuarios debe seguir también a la creación de este tipo de soluciones. En este
contexto, esta tesis descubre y desvela resultados impactantes sobre el uso de la información
personal: (i) cuantifica el número de usuarios afectados por el uso de atributos sensibles
utilizados para la publicidad en Facebook, utilizando como referencia la definición de datos
sensibles del Reglamento General de Protección de Datos. Esta tesis se basa en el uso de
Procesamiento de Lenguaje Natural para identificar los atributos sensibles, y posteriormente
utiliza el la plataforma de creación de anuncios de Facebook para recuperar el número de
usuarios asignados con esta información sensible. Dos tercios de los usuarios de Facebook
se ven afectados por el uso de datos personales sensibles que se les atribuyen. Además, la
legislación parece no tener efecto en este uso de atributos sensibles por parte de Facebook, y
presenta graves riesgos para los usuarios. (ii) Se modela cuál es el número de atributos que
no identifican a priori personalmente al usuario y que aun así son suficientes para identificar
de forma única a un individuo sobre una base de datos de miles de millones de usuarios,
y se demuestra que llegar a un solo usuario es plausible incluso sin conocer datos que lo
identifiquen personalmente de ellos mismos. Los resultados demuestran que 22 intereses al
azar de un usuario son suficientes para identificarlo unívocamente con un 90% de probabilidad,
y 4 si tomamos los menos populares.
Por último, esta tesis se ha visto afectada por el estallido de la pandemia del COVID-
19, lo que ha contribuido al análisis de la evolución del mercado de la publicidad en línea
con este periodo. La investigación demuestra que el mercado de la publicidad muestra una
inelasticidad casi perfecta en la oferta y que cambió su composición debido a un cambio en el
comportamiento en línea de los usuarios. También ilustra el potencial que tiene la utilización
de los datos de los grandes servicios en línea, dado que ya tienen una alta tasa de adopción,
y presenta un protocolo para la localización de contactos que han estado potencialmente expuestos a personas que direon positivo en COVID-19, en contraste con el fracaso de las
nuevas aplicaciones de localización de contactos.
En conclusión, la investigación de esta tesis muestra el impacto social y económico de la
publicidad online y de los grandes servicios online en los usuarios. La metodología utilizada y
desplegada sirve para poner de manifiesto y cuantificar los riesgos derivados de los datos personales
en los servicios en línea. Presenta la necesidad de tales herramientas y metodologías
en consonancia con la nueva legislación y los deseos de los usuarios. Siguiendo estas peticiones,
en la búsqueda de transparencia y privacidad, esta tesis muestra soluciones y medidas
fácilmente implementables para prevenir estos riesgos y capacitar al usuario para controlar su
información personal.
The web ecosystem is enormous, and overall it is sustained by an intangible attribute that mainly supports the majority of free services: the exploitation of personal information. Over the years, concerns on how services use personal data have increased and attracted the attention of media and users. This collection of personal information is the primary source of revenue on the Internet nowadays. Furthermore, on top of this, online advertising is the piece that supports it all. Without the existence of personal data in communion with online advertising, the Internet would probably not be the giant we know today. Online advertising is a very complex ecosystem in which multiple stakeholders take part. It is the motor that generates revenue on the web, and it has evolved in a few years to reach billions of users worldwide. While browsing, users generate valuable data about themselves that advertisers later use to offer them relevant products in which users could be interested. It is a two-way approach since advertisers pay intermediates to show ads to the public that is, in principle, most interested. However, this trading, sharing, and processing of personal data and behavior patterns, apart from opening up new advertising ways, expose users’ privacy. This incessant collection and commercialization of personal information usually fall behind an opaque wall, where the user often does not know what their data is used for. Privacy and transparency initiatives have increased over the years to empower the user in this business that moves billions of US dollars in revenue. Not surprisingly, after several scandals, such as the Facebook Cambridge Analytica scandal, businesses and regulators have joined forces to create transparency and protect users against the harmful practices derived from the use of their personal information. For instance, the General Data Protection Regulation (GDPR), is the most promising example of a data protection regulation, affecting all the member states of the European Union (EU), advocating for protecting users. The content of this thesis will use this legislation as a reference. For all these reasons, the purpose of this thesis is to provide tools and methodologies that reveal inappropriate uses of personal data by large companies in the online advertising ecosystem and create transparency among users, providing solutions to protect themselves. Thus, the content of this thesis offers design, analysis, and implementation of methodologies that measure online personal information’s social and economic impact on extensive Internet services. Mainly, it focuses on Facebook (FB), one of the largest social networks and services on the web, accounting with more than 2.8B Monthly Active Users (MAU) worldwide and generating only in online advertising revenue, more than $84B in 2020. First, this thesis presents a solution, in the form of a browser extension called Data Valuation Tool for Facebook users (FDVT), to provide users with a personalized, real-time estimation of the money they are generating for FB. By analyzing the number of ads and interactions in a session, the user gets information on their value within this social network. The add-on has had significant impact and adoption both by users, being installed more than 10k times since its public launch in October 2016, and media, appearing in more than 100 media outlets. Second, the study and research of the potential risks associated with processing users’ data should also follow the creation of these kinds of solutions. In this context, this thesis discovers and unveils striking results on the usage of personal information: (i) it quantifies the number of users affected by the usage of sensitive attributes used for advertising on FB, using as reference the definition of sensitive data from the GDPR. This thesis relies on the use of Natural Language Processing (NLP) to identify sensitive attributes, and it later uses the FB Ads Manager to retrieve the number of users assigned with this sensitive information. Two-thirds of FB users are affected by the use of sensitive personal data attributed to them. Moreover, the legislation seems not to affect this use of sensitive attributes from FB, and it presents severe risks to users. (ii) It models the number of non-Personal Identifiable Information (PII) attributes that are enough to uniquely identify an individual over a database of billions of users and proofs that reaching a single user is plausible even without knowing PII data of themselves. The results demonstrate that 22 interests at random from a user are enough to identify them uniquely with a 90% of probability, and 4 when taking the least popular ones. Finally, this thesis was affected by the outbreak of the COVID-19 pandemic what led to side contribute to the analysis of how the online advertising market evolved during this period. The research shows that the online advertising market shows an almost perfect inelasticity on supply and that it changed its composition due to a change in users’ online behavior. It also illustrates the potential of using data from large online services which already have a high adoption rate and presents a protocol for contact tracing individuals who have been potentially exposed to people who tested positive in COVID-19, in contrast to the failure of newly deployed contact tracing apps. In conclusion, the research for this thesis showcases the social and economic impact of online advertising and extensive online services on users. The methodology used and deployed is used to highlight and quantify the risks derived from personal data in online services. It presents the necessity of such tools and methodologies in line with new legislation and users’ desires. Following these requests, in the search for transparency and privacy, this thesis displays easy implementable solutions and measurements to prevent these risks and empower the user to control their personal information.
The web ecosystem is enormous, and overall it is sustained by an intangible attribute that mainly supports the majority of free services: the exploitation of personal information. Over the years, concerns on how services use personal data have increased and attracted the attention of media and users. This collection of personal information is the primary source of revenue on the Internet nowadays. Furthermore, on top of this, online advertising is the piece that supports it all. Without the existence of personal data in communion with online advertising, the Internet would probably not be the giant we know today. Online advertising is a very complex ecosystem in which multiple stakeholders take part. It is the motor that generates revenue on the web, and it has evolved in a few years to reach billions of users worldwide. While browsing, users generate valuable data about themselves that advertisers later use to offer them relevant products in which users could be interested. It is a two-way approach since advertisers pay intermediates to show ads to the public that is, in principle, most interested. However, this trading, sharing, and processing of personal data and behavior patterns, apart from opening up new advertising ways, expose users’ privacy. This incessant collection and commercialization of personal information usually fall behind an opaque wall, where the user often does not know what their data is used for. Privacy and transparency initiatives have increased over the years to empower the user in this business that moves billions of US dollars in revenue. Not surprisingly, after several scandals, such as the Facebook Cambridge Analytica scandal, businesses and regulators have joined forces to create transparency and protect users against the harmful practices derived from the use of their personal information. For instance, the General Data Protection Regulation (GDPR), is the most promising example of a data protection regulation, affecting all the member states of the European Union (EU), advocating for protecting users. The content of this thesis will use this legislation as a reference. For all these reasons, the purpose of this thesis is to provide tools and methodologies that reveal inappropriate uses of personal data by large companies in the online advertising ecosystem and create transparency among users, providing solutions to protect themselves. Thus, the content of this thesis offers design, analysis, and implementation of methodologies that measure online personal information’s social and economic impact on extensive Internet services. Mainly, it focuses on Facebook (FB), one of the largest social networks and services on the web, accounting with more than 2.8B Monthly Active Users (MAU) worldwide and generating only in online advertising revenue, more than $84B in 2020. First, this thesis presents a solution, in the form of a browser extension called Data Valuation Tool for Facebook users (FDVT), to provide users with a personalized, real-time estimation of the money they are generating for FB. By analyzing the number of ads and interactions in a session, the user gets information on their value within this social network. The add-on has had significant impact and adoption both by users, being installed more than 10k times since its public launch in October 2016, and media, appearing in more than 100 media outlets. Second, the study and research of the potential risks associated with processing users’ data should also follow the creation of these kinds of solutions. In this context, this thesis discovers and unveils striking results on the usage of personal information: (i) it quantifies the number of users affected by the usage of sensitive attributes used for advertising on FB, using as reference the definition of sensitive data from the GDPR. This thesis relies on the use of Natural Language Processing (NLP) to identify sensitive attributes, and it later uses the FB Ads Manager to retrieve the number of users assigned with this sensitive information. Two-thirds of FB users are affected by the use of sensitive personal data attributed to them. Moreover, the legislation seems not to affect this use of sensitive attributes from FB, and it presents severe risks to users. (ii) It models the number of non-Personal Identifiable Information (PII) attributes that are enough to uniquely identify an individual over a database of billions of users and proofs that reaching a single user is plausible even without knowing PII data of themselves. The results demonstrate that 22 interests at random from a user are enough to identify them uniquely with a 90% of probability, and 4 when taking the least popular ones. Finally, this thesis was affected by the outbreak of the COVID-19 pandemic what led to side contribute to the analysis of how the online advertising market evolved during this period. The research shows that the online advertising market shows an almost perfect inelasticity on supply and that it changed its composition due to a change in users’ online behavior. It also illustrates the potential of using data from large online services which already have a high adoption rate and presents a protocol for contact tracing individuals who have been potentially exposed to people who tested positive in COVID-19, in contrast to the failure of newly deployed contact tracing apps. In conclusion, the research for this thesis showcases the social and economic impact of online advertising and extensive online services on users. The methodology used and deployed is used to highlight and quantify the risks derived from personal data in online services. It presents the necessity of such tools and methodologies in line with new legislation and users’ desires. Following these requests, in the search for transparency and privacy, this thesis displays easy implementable solutions and measurements to prevent these risks and empower the user to control their personal information.
Description
Keywords
Data Valuation Tool for Facebook users (FDVT), Personal data, Data privacy, Security and privacy, Contact tracing, Collaborative and social computing systems and tools, World Wide Web, Facebook