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

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.

References

1. Ackerman, M. and Mainwaring, S. (2005). Privacy Issues in Human-Computer Interaction. In L. Cranor and S. Garfinkel (Eds.), Security and Usability: Designing Secure Systems that People Can Use, 381--400, Sebastopol, CA, O'Reilly, 2005.
2. boyd, d. m. & Ellison, N. B. (2008). Social network sites: Definition, history and scholarship. Journal of Computer--Mediated Communication, 13(1), 210--230.
3. boyd, d., & Heer, J. (2006). Profiles as conversation: Networked identity performance on friendster. In Hawaii international conference on systems science (Vol. (HICSS-39)). Kauai, HI: IEEE Computer Society.
4. Clauset, A., Newman, M., and Moore, C. (2004). Finding community structure in very large networks. Phys Rev E 2004, 70:066111.
5. Davis, M., Canny, J., Van House, N., Good, N., King, S., Nair, R., Burgener, C., Rinehart, B., Strickland, R., Campbell, G., Fisher, S., and Reid, N. (2005). MMM2: mobile media metadata for media sharing. In Proceedings of the 13th Annual ACM international Conference on Multimedia (Hilton, Singapore, November 06--11, 2005). MULTIMEDIA '05. ACM, New York, NY, 267--268.
6. DiMicco, J. M. and Millen, D. R. (2007). Identity management: multiple presentations of self in facebook. In Proceedings of the 2007 international ACM Conference on Supporting Group Work (Sanibel Island, Florida, USA, November 04--07, 2007). GROUP '07. ACM, New York, NY, 383--386.
7. Fisher, D. (2005). Using egocentric networks to understand communication. IEEE Internet Computing, 9(5), 20--28.
8. Gilbert, E. and Karahalios, K. (2009). Predicting tie strength with social media. In CHI '09: Proceedings of the 27th international conference on Human factors in computing systems, New York, NY, USA, pp. 211--220. ACM.
9. Goffman, E. (1959). The Presentation of Self in Everyday Life. New York: Doubleday.
10. Granovetter, M. S. (1973). The Strength of Weak Ties. The American Journal of Sociology, 78(6), 1360--1380.
11. Hewitt, A. and Forte, A. (2006), 'Crossing boundaries: Identity management and student/faculty relationships on the Facebook', CSCW06, November 4--8.
12. Hogan, B. (2008). A Comparison of On and Offline Networks through the Facebook API (December 18, 2008). Available at SSRN: http://ssrn.com/abstract=1331029
13. Jones, Q., Gandhi, S. A., Whittaker, S., Chivakula, K., and Terveen, L. (2004). Putting Systems into Place: A Qualitative Study of Design Requirements for Location-Aware Community Systems, In Proceedings of Computer-Supported Cooperative Work (CSCW '04), Chicago, ACM Press, pg. 202--211, 2004.
14. Lampinen, A., Tamminen, S., and Oulasvirta, A. (2009). All My People Right Here, Right Now: management of group co-presence on a social networking site. In Proceedings of the ACM 2009 international Conference on Supporting Group Work (Sanibel Island, Florida, USA, May 10--13, 2009). GROUP '09. ACM, New York, NY, 281--290
15. Lederer, S. Dey, A. K., & Mankoff, J. (2002). "A conceptual model and a metaphor of everyday privacy in ubiquitous computing," Intel Research Berkeley, Tech. Rep. IRB-TR-02-017, 2002.
16. Lederer, S., Hong, J., Dey, A., & Landay, J. (2004). 'Personal privacy through understanding and action: five pitfalls for designers', Personal and Ubiquitous Computing, vol. 8, no. 6, 440--454.
17. McCarty, C. (2002). Structure in personal networks. Journal of Social Structure, 3.
18. Olson, J., Grudin, J., & Horvitz, E. (2005). A study of preferences for sharing and privacy. In: CHI '05 extended abstracts on human factors in computing systems, Portland, OR, USA, pp 1985--1988.
19. Skeels, M. M. and Grudin, J. (2009). When social networks cross boundaries: a case study of workplace use of facebook and linkedin. In Proceedings of the ACM 2009 international Conference on Supporting Group Work (Sanibel Island, Florida, USA, May 10--13, 2009). GROUP '09. ACM, New York, NY, 95--104.
20. Torres, G., Basnet, R., Sung, A., Mukkamala, S., and Ribiero, B. (2008). A Similarity Measure for Clustering and its Applications. Proceedings of World Academy of Science, Engineering and Technology, Vol. 31 (JULY 2008) ISSN 1307--6884, pp. 490--496.
21. Xu, X., Yuruk, N., Feng, Z., and Schweiger, TA. (2007). SCAN: a structural clustering algorithm for networks. In Proceedings of the 13th ACM SIGKDD international Conference on Knowledge Discovery and Data Mining. KDD '07 ACM, New York, NY. pp. 824--833.

Index Terms

• Human-centered computing

  • Collaborative and social computing

    • Collaborative and social computing systems and tools

      • Asynchronous editors

  • Human computer interaction (HCI)

    • Interaction paradigms

      • Web-based interaction