46 Citations (Scopus)

Abstract

As (one's) social network expands, a user's privacy protec- tion goes beyond his privacy settings and becomes a social networking problem. In this research, we aim to address some critical issues related to privacy protection: Would the highest privacy settings guarantee a secure protection? Given the open nature of social networking sites, is it possible to manage one's privacy protection? With the diversity of one's social media friends, how can one figure out an effective approach to balance between vulnerability and privacy? We present a novel way to define a vulnerable friend from an individual user's perspective is dependent on whether or not the user's friends'privacy settings protect the friend and the individual's network of friends (which includes the user). As a single vulnerable friend in a user's social network might place all friends at risk, we resort to experiments and observe how much security an individual user can improve by unfriending a vulnerable friend. We also show how privacy weakens if newly accepted friends are unguarded or unprotected. This work provides a large-scale evaluation of new security and privacy indexes using a Facebook dataset. We present and discuss a new perspective for reasoning about social networking security. When a user accepts a new friend, the user should ensure that the new friend is not an increased security risk with the potential of negatively impacting the entire friend network. Additionally, by leveraging the indexes proposed and employing new strategies for unfriending vulnerable friends, it is possible to further improve security and privacy without changing the social networking site's existing architecture.

Original languageEnglish (US)
Title of host publicationProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Pages511-519
Number of pages9
DOIs
StatePublished - 2011
Event17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD'11 - San Diego, CA, United States
Duration: Aug 21 2011Aug 24 2011

Other

Other17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD'11
CountryUnited States
CitySan Diego, CA
Period8/21/118/24/11

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Experiments

Keywords

  • Experimentation
  • Security

ASJC Scopus subject areas

  • Software
  • Information Systems

Cite this

Gundecha, P., Barbier, G., & Liu, H. (2011). Exploiting vulnerability to secure user privacy on a social networking site. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 511-519) https://doi.org/10.1145/2020408.2020489

Exploiting vulnerability to secure user privacy on a social networking site. / Gundecha, Pritam; Barbier, Geoffrey; Liu, Huan.

Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2011. p. 511-519.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Gundecha, P, Barbier, G & Liu, H 2011, Exploiting vulnerability to secure user privacy on a social networking site. in Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. pp. 511-519, 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD'11, San Diego, CA, United States, 8/21/11. https://doi.org/10.1145/2020408.2020489
Gundecha P, Barbier G, Liu H. Exploiting vulnerability to secure user privacy on a social networking site. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2011. p. 511-519 https://doi.org/10.1145/2020408.2020489
Gundecha, Pritam ; Barbier, Geoffrey ; Liu, Huan. / Exploiting vulnerability to secure user privacy on a social networking site. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2011. pp. 511-519
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