Cross-jurisdictional criminal activity networks to support border and transportation security

2015

Secur Inform

Duplicate and false identity records are quite common in identity management systems due to unintentional errors or intentional deceptions. Identity resolution is to uncover identity records that are co-referent to the same real-world individual. In this paper we introduce a framework of identity resolution that covers different identity attributes and matching algorithms. Guided by social identity theories, we define three types of identity cues, namely personal identity attributes, social behavior attributes, and social relationship attributes. We also compare three matching algorithms: pair-wise comparison, transitive closure, and collective clustering. Our experiments using synthetic and real-world data demonstrate the importance of social behavior and relationship attributes for identity resolution. In particular, a collective identity resolution technique, which captures all three types of identity attributes and makes matching decisions on identities collectively, is shown to achieve the best performance among all approaches.

Section: Resolution Techniquesmentioning

confidence: 99%

A framework of identity resolution: evaluating identity attributes and matching algorithms

2015

Secur Inform

“…For instance, in a study on cross-jurisdictional information integration, Marshall et al (2004) encode domain experts' heuristics into a simple rule: two identity records match only if their first name, last name, and DOB values are all identical. Obviously, in the case of data quality problems including missing values, such exactmatch heuristics will cause many false negative decisions and cannot effectively identify matching identity records.…”

Section: Heuristic Approaches For Identity Matchingmentioning

confidence: 99%

Identity matching using personal and social identity features

Chen

2010

Inf Syst Front

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.

“…Marshall et al [3] provided an exact-value matching technique for law enforcement applications. Two identities are considered matching only if their name and DOB values are identical.…”

Section: Identity Matching Techniquesmentioning

confidence: 99%

“…In our experiments we compared the performance of our proposed model against that of other existing identity matching techniques, namely the exact-match based technique [3] and the record comparison algorithm [6]. We also evaluated the performance differences of our model in three different learning modes: supervised, semi-supervised, and unsupervised learning.…”

Section: Experimental Designmentioning

confidence: 99%

A probabilistic model for approximate identity matching

Proceedings of the 2006 International Conference on Digital Government Research

Chen

Atabakhsh

2006

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.