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Identity matching using personal and social identity features

Author

Listed:
  • Jiexun Li

    (Drexel University)

  • G. Alan Wang

    (Virginia Tech)

  • Hsinchun Chen

    (University of Arizona)

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.

Suggested Citation

  • Jiexun Li & G. Alan Wang & Hsinchun Chen, 2011. "Identity matching using personal and social identity features," Information Systems Frontiers, Springer, vol. 13(1), pages 101-113, March.
    • Handle: RePEc:spr:infosf:v:13:y:2011:i:1:d:10.1007_s10796-010-9270-0
    DOI: 10.1007/s10796-010-9270-0
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    References listed on IDEAS

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    1. Debabrata Dey & Sumit Sarkar & Prabuddha De, 1998. "A Probabilistic Decision Model for Entity Matching in Heterogeneous Databases," Management Science, INFORMS, vol. 44(10), pages 1379-1395, October.
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    Cited by:

    1. Ahmed Aleroud & Aryya Gangopadhyay, 2018. "Multimode co-clustering for analyzing terrorist networks," Information Systems Frontiers, Springer, vol. 20(5), pages 1053-1074, October.
    2. Ruochen Liao & Shenaz Balasinorwala & H. Raghav Rao, 2017. "Computer assisted frauds: An examination of offender and offense characteristics in relation to arrests," Information Systems Frontiers, Springer, vol. 19(3), pages 443-455, June.
    3. Jorge Martinez-Gil & José F. Aldana-Montes, 2013. "Semantic similarity measurement using historical google search patterns," Information Systems Frontiers, Springer, vol. 15(3), pages 399-410, July.
    4. Chulhwan Chris Bang, 2015. "Information systems frontiers: Keyword analysis and classification," Information Systems Frontiers, Springer, vol. 17(1), pages 217-237, February.
    5. Ahmed Aleroud & Aryya Gangopadhyay, 0. "Multimode co-clustering for analyzing terrorist networks," Information Systems Frontiers, Springer, vol. 0, pages 1-22.
    6. Hsinchun Chen & Yilu Zhou & Edna F. Reid & Catherine A. Larson, 2011. "Introduction to special issue on terrorism informatics," Information Systems Frontiers, Springer, vol. 13(1), pages 1-3, March.

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