Analysis, modelling and protection of online private data

• Autores: Silvia Puglisi
• Directores de la Tesis: David Rebollo Monedero (dir. tes.), Jordi Forné Muñoz (dir. tes.)
• Lectura: En la Universitat Politècnica de Catalunya (UPC) ( España ) en 2017
• Idioma: español
• Tribunal Calificador de la Tesis: Mónica Aguilar Igartua (presid.), Helena Rifà Pous (secret.), Jordi Castellà Roca (voc.)
• Materias:
  • Ciencias tecnológicas
    • Tecnología de los ordenadores
      • Dispositivos de transmisión de datos
• Enlaces
  • Tesis en acceso abierto en: TDX
• Resumen

  • Online communications generate a consistent amount of data flowing between users, services and applications. This information results from the interactions among different parties, and once collected, it is used for a variety of purposes, from marketing profiling to product recommendations, from news filtering to relationship suggestions.

    Understanding how data is shared and used by services on behalf of users is the motivation behind this work. When a user creates a new account on a certain platform, this creates a logical container that will be used to store the user's activity. The service aims to profile the user. Therefore, every time some data is created, shared or accessed, information about the user¿s behaviour and interests is collected and analysed. Users produce this data but are unaware of how it will be handled by the service, and of whom it will be shared with. More importantly, once aggregated, this data could reveal more over time that the same users initially intended. Information revealed by one profile could be used to obtain access to another account, or during social engineering attacks.

    The main focus of this dissertation is modelling and analysing how user data flows among different applications and how this represents an important threat for privacy. A framework defining privacy violation is used to classify threats and identify issues where user data is effectively mishandled. User data is modelled as categorised events, and aggregated as histograms of relative frequencies of online activity along predefined categories of interests. Furthermore, a paradigm based on hypermedia to model online footprints is introduced. This emphasises the interactions between different user-generated events and their effects on the user¿s measured privacy risk. Finally, the lessons learnt from applying the paradigm to different scenarios are discussed.