Identity De-duplication via Network Analysis
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Solution Overview
Problem
Current law enforcement systems face challenges in eliminating data duplication, particularly due to misspelling of names and inadequate preventative measures, leading to inaccurate identification of duplicate identities, as existing methods are static and do not account for variability or human error.
Innovation Solution
The implementation of network analysis and behavioral comparisons, which examine extended networks and centrality measures to verify duplicate identities, increasing accuracy by considering multiple degrees of separation and social network importance, rather than relying solely on facial image or string comparisons.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If static comparison methods (facial image or string comparison) are used to identify duplicate identities, then the system is simple to operate, but the accuracy of duplicate detection is low and cannot account for variability or human error
Solution Approach 1:
The patent transitions from static two-dimensional comparison (facial images or name strings) to multi-dimensional network analysis that examines relationships across multiple degrees of separation. By adding the dimension of social network connections and behavioral patterns, the system achieves higher detection accuracy while managing complexity through structured network modeling
Solution Approach 2:
The identification system is segmented into multiple independent analysis components: network structure analysis, behavioral pattern comparison, centrality measure calculation, and similarity scoring. Each component handles a specific aspect of duplicate detection, allowing the system to achieve high accuracy through coordinated analysis while maintaining operational manageability through modular design
2Measurement precision
If network analysis examining extended networks and multiple degrees of separation is used, then the accuracy of identifying duplicate identities is significantly enhanced, but the computational complexity and data processing requirements increase
Solution Approach 1:
The system performs preliminary actions by pre-calculating and storing network centrality measures, connection counts, and behavioral baselines for each individual in the database. This preprocessing allows the duplicate detection algorithm to compare pre-computed metrics rather than analyzing entire network structures in real-time, significantly reducing computational complexity while maintaining high detection accuracy
Solution Approach 2:
The patent changes the parameters being analyzed from raw network data to derived metrics such as centrality scores, connection density, and behavioral deviation thresholds. By transforming complex network structures into standardized parameters with defined ranges and significance thresholds, the system achieves high measurement precision while simplifying the computational comparison process
Data Source
AI summary
A processing device identifies comparison subjects comprising a first subject and a second subject. The processing device builds a first network of a first profile of the first subject and a second network of a second profile of the second subject, wherein the first network and the second network are multi-degree connections networks. The processing device the first network and second network to produce a similarity score. The processing device examining first-degree connections between the first network and the second network and accounting for centralities that rely on information about the first network and the second network. Responsive to the similarity score exceeding a similarity threshold, indicating the first profile and the second profile correspond to a common profile for the first subject and the second subject.