Identity matching using personal and social identity features

Jiexun Li, G. Alan Wang, Hsinchun Chen

Research output: Contribution to journalArticle

22 Scopus citations

Abstract

Identity verification is essential in our mission to identify potential terrorists and criminals. It is not a trivial task because terrorists reportedly assume multiple identities using either fraudulent or legitimate means. A national identification card and biometrics technologies have been proposed as solutions to the identity problem. However, several studies show their inability to tackle the complex problem. We aim to develop data mining alternatives that can match identities referring to the same individual. Existing identity matching techniques based on data mining primarily rely on personal identity features. In this research, we propose a new identity matching technique that considers both personal identity features and social identity features. We define two groups of social identity features including social activities and social relations. The proposed technique is built upon a probabilistic relational model that utilizes a relational database structure to extract social identity features. Experiments show that the social activity features significantly improve the matching performance while the social relation features effectively reduce false positive and false negative decisions.

Original languageEnglish (US)
Pages (from-to)101-113
Number of pages13
JournalInformation Systems Frontiers
Volume13
Issue number1
DOIs
StatePublished - 2011

Keywords

  • Identity management
  • Identity matching
  • Probabilistic relational model
  • Social context

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Software
  • Information Systems
  • Computer Networks and Communications

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