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A probabilistic model for approximate identity matching

Published: 21 May 2006 Publication History

Abstract

Identity management is critical to various governmental practices ranging from providing citizens services to enforcing homeland security. The task of searching for a specific identity is difficult because multiple identity representations may exist due to issues related to unintentional errors and intentional deception. We propose a probabilistic Naïve Bayes model that improves existing identity matching techniques in terms of effectiveness. Experiments show that our proposed model performs significantly better than the exact-match based technique as well as the approximate-match based record comparison algorithm. In addition, our model greatly reduces the efforts of manually labeling training instances by employing a semi-supervised learning approach. This training method outperforms both fully supervised and unsupervised learning. With a training dataset that only contains 10% labeled instances, our model achieves a performance comparable to that of a fully supervised learning.

References

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Camp, J., "Identity in Digital Government," presented at 2003 Civic Scenario Workshop: An Event of the Kennedy School of Government, (Cambridge, MA 02138, 2003)
[2]
Dey, D., Sarkar, S., and De, P., "A Distance-Based Approach to Entity Reconciliation in Heterogeneous Databases," IEEE Transactions on Knowledge and Data Engineering, vol, 14 2002, pp. 567--582, 2002.
[3]
Marshall, B., Kaza, S., Xu, J., Atabakhsh, H., Petersen, T., Violette, C., and Chen, H., "Cross-Jurisdictional criminal activity networks to support border and transportation security," presented at 7th Annual IEEE Conference on Intelligent Transportation Systems (ITSC 2004), (Washington, D.C., 2004)
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Nigam, K., McCallum, A. K., Thrun, S., and Mitchell, T., "Text Classification from Labeled and Unlabeled Documents using EM," Machine Learning, vol, 39 2000), pp. 103--134, 2000.
[5]
Ravikumar, P. and Cohen, W. W., "A Hierarchical Graphical Model for Record Linkage," presented at 20th Conference on Uncertainty in Artificial Intelligence (UAI '04), (Banff Park Lodge, Banff, Canada, 2004)
[6]
Wang, G., Chen, H., and Atabakhsh, H., "Automatically Detecting Deceptive Criminal Identities," Communications of the ACM, vol, 47 2004, pp. 71--76, 2004.
[7]
Wang, G. A., Atabakhsh, H., Petersen, T., and Chen, H., "Discovering Identity Problems: A Case Study," in Intelligence and Security Informatics: IEEE International Conference on Intelligence and Security Informatics (ISI 2005). Atlanta, GA, 2005.

Cited By

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  • (2011)Design and implementation of multimodal digital identity management system using fingerprint matching and face recognition7th International Conference on Broadband Communications and Biomedical Applications10.1109/IB2Com.2011.6217932(272-278)Online publication date: Nov-2011
  • (2009)Identity Management ArchitectureSecurity Informatics10.1007/978-1-4419-1325-8_6(97-116)Online publication date: 12-Oct-2009

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Published In

cover image ACM Other conferences
dg.o '06: Proceedings of the 2006 international conference on Digital government research
May 2006
526 pages

Sponsors

  • NSF: National Science Foundation

Publisher

Digital Government Society of North America

Publication History

Published: 21 May 2006

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Author Tags

  1. identity matching
  2. naïve bayes model
  3. semi-supervised learning

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  • Article

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dg.o '06
Sponsor:
  • NSF
dg.o '06: Digital government research
May 21 - 24, 2006
California, San Diego, USA

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dg.o '06 Paper Acceptance Rate 20 of 58 submissions, 34%;
Overall Acceptance Rate 150 of 271 submissions, 55%

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Cited By

View all
  • (2011)Design and implementation of multimodal digital identity management system using fingerprint matching and face recognition7th International Conference on Broadband Communications and Biomedical Applications10.1109/IB2Com.2011.6217932(272-278)Online publication date: Nov-2011
  • (2009)Identity Management ArchitectureSecurity Informatics10.1007/978-1-4419-1325-8_6(97-116)Online publication date: 12-Oct-2009

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