Automating Group-Based Privacy Control in Social Networks

  • Simon Jones

Student thesis: Doctoral ThesisPhD


Users of social networking services such as Facebook often want to manage thesharing of information and content with different groups of people based on theirdiffering relationships. The growing popularity of such services has meant thatusers are increasingly faced with the copresence of different groups associatedwith different aspects of their lives, within their network of contacts. However,few users are utilising the group-based privacy controls provided to them by theSNS provider. In this thesis we examine the reasons behind the lack of use ofgroup-based privacy controls, finding that it can be largely attributed to thesignificant burden associated with group configuration. We aim to overcome thisburden by developing automated mechanisms to assist users with many aspectsof group-based privacy control, including initial group configuration, labeling,adjustment and selection of groups for sharing privacy sensitive content.We use a mixed methods approach in order to understand: how automatedmechanisms should be designed in order to support users with their privacy control, how well these mechanisms can be expected to work, what the limitations are, and how such mechanisms affect users’ experiences with social networking services and content sharing. Our results reveal the criteria that SNS users employ in order to configure their groups for privacy control and illustrate that off-the-shelf algorithms and techniques which are analogous to these criteria can be used to support users. We show that structural network clustering algorithms provide benefits for initial group configuration and that clustering threshold adjustments and detection of hubs and outliers with the network are necessary for group adjustment. We demonstrate that public profile data can be extracted from the network in order to help users to comprehend their groups, and that contextual information relating to context, contacts, and content can be used to make recommendations about which groups might be useful for disclosure in a given situation. We also show that all of these mechanisms can be used to significantly reduce the burden of privacy control and that users react positively to such features.
Date of Award1 Aug 2012
Original languageEnglish
Awarding Institution
  • University of Bath
SupervisorEamonn O'Neill (Supervisor)


  • Social network analysis
  • privacy
  • human computer interaction

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