Identity Confidence Framework

• US 20100250628A1
• Filed: 03/02/2010
• Published: 09/30/2010
• Est. Priority Date: 03/05/2009
• Status: Abandoned Application

• Current Assignee: FusionArc Incorporated
• Original Assignee: FusionArc Incorporated
• Inventors: Coleman, David, Nanavati, Samir, Nanavati, Rajkumar
• Application Number:
  US12/715,750
• Publication Number:
  US 20100250628A1
• Time in Patent Office: Days
• US Class Current: 707/812
• CPC Class Codes: G06F 21/31 User authentication
• CPC Class Codes: G06F 2221/2145 Inheriting rights or properties, e.g., propagation of permissions or restrictions within a hierarchy
• CPC Class Codes: G06Q 10/00 Administration; Management
• CPC Class Codes: G06Q 50/26 Government or public services business processes related to the transportation industry G06Q50/40
• CPC Class Codes: H04L 63/102 Entity profiles

Abstract

An Identity Confidence Framework is a method for determining the confidence that each of various identity elements pertain to the same person.

Claims

1. A method of storing identity elements suspected of belonging to a subject in a structured data model.

2. The method of claim 1, where the structured data model is a graph.

3. The method of claim 2, where the graph is an undirected graph.

4. The method of claim 3, where a single node (subject-node) is assigned to each subject.

5. The method of claim 4, where other nodes (element-nodes) are assigned to identity elements and are assigned a weight corresponding to confidence in the element'"'"'s authenticity.

6. The method of claim 5, where each edge (binding-edge) between an element-node and a subject-node is assigned a weight corresponding to confidence that the element pertains to the given subject.

7. The method of claim 6, where each edge (link-edge) between an element-node and another element-node is assigned a weight corresponding to confidence that the two elements pertain to the same subject.

8. The method of claim 7, where an overall confidence is computed for each identity-element, based on other edge and node weightings in the graph.

9. The method of claim 2, where the graph is a directed graph.

10. The method of claim 9, where a single node (subject-node) is assigned to the subject.

11. The method of claim 10, where other nodes (element-node) are assigned to identity elements.

12. The method of claim 11, where each edge (binding-edge) between an element-node and a subject-node is assigned a weight corresponding to confidence that the element pertains to the given subject.

13. The method of claim 12, where each edge (link-edge) between an element-node and another element-node is assigned a weight corresponding to confidence that the two elements pertain to the same subject and that the destination element is authentic.

14. The method of claim 13, where each for each link-edge (x,y) between element-node x and element-node y there is a corresponding edge (y,x).

15. The method of claim 14, where an overall confidence is computed for each identity-element, based on edge weightings in the graph.

16. The method of claim 1, where the structured data model is selected from the set of matrix, list, database, database table, or file system.

17. The method of claim 1, where the structured data model is a non-graph object model.