4.4 Article

Modeling Unintended Personal-Information Leakage from Multiple Online Social Networks

Journal

IEEE INTERNET COMPUTING
Volume 15, Issue 3, Pages 13-19

Publisher

IEEE COMPUTER SOC
DOI: 10.1109/MIC.2011.25

Keywords

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Funding

  1. The US National Science Foundation (NSF) through its Industry-University Cooperative Research Center
  2. CyberTrust
  3. CISE/Computing Research Infrastructure
  4. NetSE
  5. US National Institutes of Health [U54 RR 024380-01, UL1 RR025008, KL2 RR025009, TL1 RR025010]
  6. Wipro Technologies
  7. Fujitsu Labs
  8. Amazon
  9. Georgia Tech Foundation
  10. Direct For Computer & Info Scie & Enginr
  11. Division Of Computer and Network Systems [0855180] Funding Source: National Science Foundation

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Most people have multiple accounts on different social networks. Because these networks offer various levels of privacy protection, the weakest privacy policies in the social network ecosystem determine how much personal information is disclosed online. A new information leakage measure quantifies the information available about a given user. Using this measure makes it possible to evaluate the vulnerability of a user's social footprint to two known attacks: physical identification and password recovery. Experiments show the measure's usefulness in quantifying information leakage from publicly crawled information and also suggest ways of better protecting privacy and reducing information leakage in the social Web.

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