ABSTRACT
Users' mental models of privacy and visibility in social networks often involve subgroups within their local networks of friends. Many social networking sites have begun building interfaces to support grouping, like Facebook's lists and "Smart Lists," and Google+'s "Circles." However, existing policy comprehension tools, such as Facebook's Audience View, are not aligned with this mental model. In this paper, we introduce PViz, an interface and system that corresponds more directly with how users model groups and privacy policies applied to their networks. PViz allows the user to understand the visibility of her profile according to automatically-constructed, natural sub-groupings of friends, and at different levels of granularity. Because the user must be able to identify and distinguish automatically-constructed groups, we also address the important sub-problem of producing effective group labels. We conducted an extensive user study comparing PViz to current policy comprehension tools (Facebook's Audience View and Custom Settings page). Our study revealed that PViz was comparable to Audience View for simple tasks, and provided a significant improvement for complex, group-based tasks, despite requiring users to adapt to a new tool. Utilizing feedback from the user study, we further iterated on our design, constructing PViz 2.0, and conducted a follow-up study to evaluate our refinements.
- A. Acquisti and R. Gross. Imagined communities: Awareness, information sharing, and privacy on the facebook. In Privacy Enhancing Technologies Workshop, 2006. Google ScholarDigital Library
- F. Adu-Oppong, C. Gardiner, A. Kapadia, and P. Tsang. Socialcircles: Tackling privacy in social networks. In SOUPS, 2008.Google Scholar
- S. Amershi, J. Fogarty, and D. S. Weld. Regroup: Interactive machine learning for on-demand group creation in social networks. In ACM Conference on Human Factors in Computing Systems (CHI): to appear, 2012. Google ScholarDigital Library
- M. Anwar, P. Fong, X.-D. Yang, and H. Hamilton. Visualizing privacy implications of access control policies in social networks. In Workshop on Data Privacy Management, 2009.Google Scholar
- A. Besmer, J. Watson, and H. Lipford. The impact of social navigation on privacy policy configuration. In SOUPS, 2010. Google ScholarDigital Library
- W. Cohen. Fast effective rule induction. In ICML, 1995.Google ScholarDigital Library
- G. Danezis. Inferring privacy policies for social networking services. In AISec, 2009. Google ScholarDigital Library
- S. Egelman, A. Oates, and S. Krishnamurthi. Oops, i did it again: Mitigating repeated access control errors on Facebook. In CHI, 2011. Google ScholarDigital Library
- L. Fang and K. LeFevre. Privacy wizards for social networking sites. In WWW, 2010. Google ScholarDigital Library
- S. Fortunato. Community detection in graphs. Physics Reports, 486, 2010.Google Scholar
- R. Gross and A. Acquisti. Information revelation and privacy in online social networks. In Workshop on Privacy in the Electronic Society, 2005. Google ScholarDigital Library
- J. Heer and d. boyd. Vizster: Visualizing online social networks. InfoVis, 2005. Google ScholarDigital Library
- S. Jones and E. O'Neill. Feasibility of structural network clustering for group-based privacy control in social networks. In SOUPS, 2010. Google ScholarDigital Library
- S. Kairam, M. J. Brzozowski, D. Huffaker, and E. H. Chi. Talking in circles: Selective sharing in google+. In ACM Conference on Human Factors in Computing Systems (CHI): to appear, 2012. Google ScholarDigital Library
- A. Lampinen, S. Tamminen, and A. Oulasvirta. All my people right here, right now: Management of group co-presence on a social networking site. In GROUP, 2009. Google ScholarDigital Library
- H. Lipford, A. Besmer, and J. Watson. Understanding privacy settings in facebook with an audience view. In Conference on Usability, Psychology, and Security, 2008. Google ScholarDigital Library
- H. Lipford, J. Watson, M. Whitney, K. Froiland, and R. Reeder. Visual vs. compact: A comparison of privacy policy interfaces. In CHI, 2010. Google ScholarDigital Library
- K. Liu and E. Terzi. A framework for computing the privacy scores of users in online social networks. In ICDM, 2009. Google ScholarDigital Library
- M. Newman and M. Girvan. Finding and evaluating community structure in networks. Physical Review, 69(2), 2004.Google Scholar
- D. Nguyen and E. Mynatt. Privacy mirrors: understanding and shaping socio-technical ubiquitous computing systems. Technical report, 2002.Google Scholar
- A. Noack. Modularity clustering is force-directed layout. Physical Review, 79(2), 2009.Google Scholar
- L. Palen and P. Dourish. Unpacking "privacy" for a networked world. In CHI, 2003. Google ScholarDigital Library
- S. Patil and J. Lai. Who gets to know what when: configuring privacy permissions in an awareness application. In CHI, 2005. Google ScholarDigital Library
- A. Perer and B. Shneiderman. Balancing systematic and flexible exploration of social networks. IEEE Transactions on Visualization and Computer Graphics, 12:693--700, 2006. Google ScholarDigital Library
- R. Reeder, L. Bauer, L. Cranor, M. Reiter, K. Bacon, K. How, and H. Strong. Expandable grids for visualizing and authoring computer security policies. In CHI, 2008. Google ScholarDigital Library
Index Terms
- The PViz comprehension tool for social network privacy settings
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