Criminal identity resolution using social behavior and relationship attributes

Jiexun Li, G. Alan Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We propose a criminal identity resolution technique that utilizes both personal identity and social identity information. Guided by existing identity theories, we examine three types of identity features, namely personal identity attributes, social behavior attributes, and social relationship attributes. We also explore three matching strategies, namely pair-wise comparison, transitive-closure, and collective resolution. Our experiment on synthetic data sets show that both social behavior and relationship attributes improve the performance of identity matching as compared to the use of personal identity attributes alone. The results also show that the collective relational resolution approach outperformed other approaches in terms of F-measure.

Original languageEnglish (US)
Title of host publicationProceedings of 2011 IEEE International Conference on Intelligence and Security Informatics, ISI 2011
Pages173-175
Number of pages3
DOIs
StatePublished - Sep 22 2011
Externally publishedYes
Event2011 IEEE International Conference on Intelligence and Security Informatics, ISI 2011 - Beijing, China
Duration: Jul 10 2011Jul 12 2011

Publication series

NameProceedings of 2011 IEEE International Conference on Intelligence and Security Informatics, ISI 2011

Conference

Conference2011 IEEE International Conference on Intelligence and Security Informatics, ISI 2011
Country/TerritoryChina
CityBeijing
Period7/10/117/12/11

Keywords

  • collective clustering
  • criminal identity resolution
  • social behaviors
  • social relationships

ASJC Scopus subject areas

  • Artificial Intelligence
  • Information Systems

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