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Feasibility of structural network clustering for group-based privacy control in social networks

Published: 14 July 2010 Publication History

Abstract

Users of social networking sites often want to manage the sharing of information and content with different groups of people based on their differing relationships. However, grouping contacts places a significant configuration burden on the user. Automated approaches to grouping may have the potential to reduce this burden, however, their use remains largely untested. We investigate people's rationales when grouping their contacts for the purpose of controlling their privacy, finding six criteria that they commonly considered. We assess an automated approach to grouping, based on a network clustering algorithm, whose performance may be analogous to the human's use of some of these criteria. We find that the similarity between the groups created by people and those created by the algorithm is correlated with the modularity of their network. We also demonstrate that the particular clustering algorithm, SCAN, which detects hubs and outliers within a network can be beneficial for identifying contacts who are hard to group or for whom privacy preferences are inconsistent with the rest of their group.

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Published In

cover image ACM Other conferences
SOUPS '10: Proceedings of the Sixth Symposium on Usable Privacy and Security
July 2010
236 pages
ISBN:9781450302647
DOI:10.1145/1837110

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  • Carnegie Mellon University: Carnegie Mellon University

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 July 2010

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Author Tags

  1. automation
  2. content sharing
  3. group-based access control
  4. network structure
  5. privacy
  6. social media
  7. social networks
  8. tie strength

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SOUPS '10
Sponsor:
  • Carnegie Mellon University
SOUPS '10: Symposium on Usable Privacy and Security
July 14 - 16, 2010
Washington, Redmond, USA

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Overall Acceptance Rate 15 of 49 submissions, 31%

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  • (2022)Understanding the Role of Context in Creating Enjoyable Co-Located InteractionsProceedings of the ACM on Human-Computer Interaction10.1145/35129786:CSCW1(1-26)Online publication date: 7-Apr-2022
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