9 Citations (Scopus)

Abstract

Privacy and security are major concerns for many users of social media. When users share information (e.g., data and photos) with friends, they can make their friends vulnerable to security and privacy breaches with dire consequences. With the continuous expansion of a user's social network, privacy settings alone are often inadequate to protect a user's profile. In this research, we aim to address some critical issues related to privacy protection: (1) How can we measure and assess individual users' vulnerability? (2) With the diversity of one's social network friends, how can one figure out an effective approach to maintaining balance between vulnerability and social utility? In this work, first we present a novel way to define vulnerable friends from an individual use's perspective. User vulnerability is dependent on whether or not the use's friends' privacy settings protect the friend and the individual's network of friends (which includes the user). We show that it is feasible to measure and assess user vulnerability and reduce one's vulnerability without changing the structure of a social networking site. The approach is to unfriend one's most vulnerable friends. However, when such a vulnerable friend is also socially important, unfriending him or her would significantly reduce one's own social status. We formulate this novel problem as vulnerability minimization with social utility constraints. We formally define the optimization problem and provide an approximation algorithm with a proven bound. Finally, we conduct a large-scale evaluation of a new framework using a Facebook dataset. We resort to experiments and observe how much vulnerability an individual user can be decreased by unfriending a vulnerable friend. We compare performance of different unfriending strategies and discuss the security risk of new friend requests. Additionally, by employing different forms of social utility, we confirm that the balance between user vulnerability and social utility can be practically achieved. This work is supported by grants of ARO (025071), ONR (N000141010091, N000141410095), and AFOSR (FA95500810132). This work was also funded, in part, by OSD-T&E (Office of Secretary Defense-Test and Evaluation), DefenseWide/PE0601120D8Z National Defense Education Program (NDEP)/BA-1, Basic Research; SMART Program Office, www.asee.org/fellowships/smart, Grant Number N00244-09-1-0081. The majority of this work was conducted when Geoffrey Barbier was affiliated with the Computer Science Denartment at Arizona Sate University.

Original languageEnglish (US)
Article number12
JournalACM Transactions on Knowledge Discovery from Data
Volume9
Issue number2
DOIs
StatePublished - Sep 23 2014

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Approximation algorithms
Computer science
Education
Experiments

Keywords

  • Privacy
  • Social network
  • Vulnerability

ASJC Scopus subject areas

  • Computer Science(all)

Cite this

User vulnerability and its reduction on a social networking site. / Gundecha, Pritam; Barbier, Geoffrey; Tang, Jiliang; Liu, Huan.

In: ACM Transactions on Knowledge Discovery from Data, Vol. 9, No. 2, 12, 23.09.2014.

Research output: Contribution to journalArticle

Gundecha, Pritam ; Barbier, Geoffrey ; Tang, Jiliang ; Liu, Huan. / User vulnerability and its reduction on a social networking site. In: ACM Transactions on Knowledge Discovery from Data. 2014 ; Vol. 9, No. 2.
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