Recommending access control decisions to social media users
Creators: name: Misra, Gaurav
Publisher: name: Lancaster University
Year: 2017

description: Social media has become an integral part of the Internet and has
      revolutionized interpersonal communication. The lines of separation
      between content creators and content consumers have blurred as normal
      users have platforms such as social media sites, blogs and microblogs at
      their disposal on which they can create and consume content as well as
      have the opportunity to interact with other users. This change has also
      led to several well documented privacy problems for the users. The privacy
      problems faced by social media users can be categorized into institutional
      privacy (related to the social network provider) and social privacy
      (related to the interpersonal communication between social media users)
      problems.
      
      The work presented in this thesis focuses on the social privacy issues
      that affect
      
      users on social media due to their interactions with members in their
      network who may represent various facets of their lives (such as work,
      family, school, etc.). In such a scenario, it is imperative for them to be
      able to appropriately control access to their information such that it
      reaches the appropriate audience. For example, a person may not want to
      share the same piece of information with their boss at work and their
      family members. These boundaries are defined by the nature of
      relationships people share with each other and are enforced by controlling
      access during communication. In real life, people are accustomed to do
      this but it becomes a greater challenge while interacting online. The
      primary contribution of the work presented in this thesis is to design an
      access control recommendation mechanism for social media users which would
      ease the burden on the user while sharing information with their contacts
      on the social network.
      
      The recommendation mechanism presented in this thesis, REACT (REcommending
      Access Control decisions To social media users), leverages information
      defining interpersonal relationships between social media users in
      conjunction with information about the content in order to appropriately
      represent the context of information disclosure. Prior research has
      pointed towards ways in which to employ information residing in the social
      network to represent social relationships between individuals. REACT
      relies on extensive empirical evaluation of such information in order to
      identify the most suitable types of information which can be used to
      predict access control decisions made by social media users. In
      particular, the work in this thesis advances the state of art in the
      following ways: (i) An empirical study to identify the most appropriate
      network based community detection algorithm to represent the type of
      interpersonal relationships in the resulting access control recommendation
      mechanism. This empirical study examines a goodness of fit of the
      communities produced by 8 popular network based community detection
      algorithms with the access control decisions made by social media users.
      (ii) Systematic feature engineering to derive the most appropriate profile
      attribute to represent the strength or closeness between social media
      users. The relationship strength is an essential indicator of access
      control preferences and the endeavor is to identify the minimal subset of
      attributes which can accurately represent this in the resulting access
      control recommendation mechanism. (iii) The suitable representation of
      interpersonal relationships in conjunction with information about the
      content that result in the design of an access control recommendation
      mechanism, REACT, which considers the overall context of information
      disclosure and is shown to produce highly accurate recommendations.