System and method for normalizing and merging credential stores

• US 8,166,072 B2
• Filed: 04/17/2009
• Issued: 04/24/2012
• Est. Priority Date: 04/17/2009
• Status: Active Grant

• Current Assignee: Taiwan Semiconductor Manufacturing Company Limited
• Original Assignee: International Business Machines Corporation
• Inventors: Kern, David Scott, Annicchiarico, Richard Francis, Kho, Nancy Ellen, Paganetti, Robert John
• Primary Examiner(s): Perveen, Rehana
• Assistant Examiner(s): Kia, Arshia S
• Application Number:
  US12/425,860
• Publication Number:
  US 20100268747A1
• Time in Patent Office: 1,103 Days
• Field of Search: None
• US Class Current: 707/786
• CPC Class Codes: H04L 63/08 for authentication of entities cryptographic mechanisms or cryptographic arrangements for entity authentication H04L9/32
• CPC Class Codes: H04L 67/1095 Replication or mirroring of data, e.g. scheduling or transport for data synchronisation between network nodes

Abstract

One or more data structures are received by a computing device, wherein the one or more data structures include at least one or more user credentials. The one or more user credentials are normalized by the computing device to generate a first graph. One or more nodes of the first graph and one or more nodes of at least a second graph are analyzed by the computing device, wherein analyzing includes at least identifying a logical correlation between the one or more nodes of the first graph and the one or more nodes of at least the second graph. A third graph is generated by the computing device based, at least in part, upon the analysis of the one or more nodes of the first graph and the one or more nodes of at least the second graph. An output data structure is generated by the computing device based, at least in part, upon the third graph.

Claims

1. A computer-implemented method comprising:

receiving, by a computing device including a processor, one or more data structures from a second computing device, wherein the one or more data structures include at least one or more user credentials;

normalizing, by the computing device, the one or more user credentials of the one or more data structures received from the second computing device to generate a first graph;

normalizing, by the computing device, one or more user credentials of one or more data structures from a storage device to generate a second graph;

analyzing, by the computing device, one or more nodes of the first graph and one or more nodes of at least the second graph, wherein analyzing includes at least identifying a logical correlation between the one or more nodes of the first graph and the one or more nodes of at least the second graph;

generating, by the computing device, a third graph based, at least in part, upon the analysis of the one or more nodes of the first graph and the one or more nodes of at least the second graph;

generating, by the computing device, an output data structure based, at least in part, upon the third graph; and

transmitting, by the computing device, the output data structure to the second computing device.

2. The computer-implemented method of claim 1 wherein the first graph is an n-dimensional sparse matrix.

3. The computer-implemented method of claim 2 wherein normalizing the one or more user credentials received from the second computing device includes:

generating one of the one or more nodes based, at least in part, upon one of the one or more user credentials received from the second computing device; and

arranging the one or more nodes, by logical level, into one or more rows of the n-dimensional sparse matrix.

4. The computer-implemented method of claim 3 wherein identifying the logical correlation between the one or more nodes of the first graph and the one or more nodes of at least the second graph includes comparing a functionality of the one or more nodes of the first graph and one or more nodes of at least the second graph.

5. The computer-implemented method of claim 3 wherein the one or more nodes include subnodes.

6. The computer-implemented method of claim 5 wherein generating the third graph based, at least in part, upon the analysis of the one or more nodes of the first graph and the one or more nodes of at least the second graph includes:

generating one or more nodes and subnodes of the third graph based, at least in part, upon one of the one or more nodes and subnodes of the first graph and the one or more nodes and subnodes of at least the second graph.

7. The computer-implemented method of claim 1 wherein the one or more user credentials includes one or more of a username, a public key, a private key, a certificate, a creation indicator, an expiration indicator, and a modification indicator.

8. The computer-implemented method of claim 1 wherein the computing device is a server computer.

9. A computer program product residing on a computer readable storage medium having a plurality of instructions stored thereon which, when executed by a processor, cause the processor to perform operations comprising:

receiving, by a computing device, one or more data structures from a second computing device, wherein the one or more data structures include at least one or more user credentials;

normalizing, by the computing device, the one or more user credentials to generate a first graph;

normalizing, by the computing device, one or more user credentials of one or more data structures from a storage device to generate a second graph;

analyzing, by the computing device, one or more nodes of the first graph and one or more nodes of at least the second graph, wherein analyzing includes at least identifying a logical correlation between the one or more nodes of the first graph and the one or more nodes of at least the second graph;

generating, by the computing device, a third graph based, at least in part, upon the analysis of the one or more nodes of the first graph and the one or more nodes of at least the second graph;

generating, by the computing device, an output data structure based, at least in part, upon the third graph; and

transmitting, by the computing device, the output data structure to the second computing device.

10. The computer program product of claim 9 wherein the first graph is an n-dimensional sparse matrix.

11. The computer program product of claim 10 wherein the instructions for normalizing the one or more user credentials received from the second computing device include:

generating one of the one or more nodes based, at least in part, upon one of the one or more user credentials received from the second computing device; and

arranging the one or more nodes, by logical level, into one or more rows of the n-dimensional sparse matrix.

12. The computer program product of claim 11 wherein the instructions for identifying the logical correlation between the one or more nodes of the first graph and the one or more nodes of at least the second graph include comparing a functionality of the one or more nodes of the first graph and one or more nodes of at least the second graph.

13. The computer program product of claim 11 wherein the one or more nodes include subnodes.

14. The computer program product of claim 13 wherein the instructions for generating the third graph based, at least in part, upon the analysis of the one or more nodes of the first graph and the one or more nodes of at least the second graph include:

generating one or more nodes and subnodes of the third graph based, at least in part, upon one of the one or more nodes and subnodes of the first graph and the one or more nodes and subnodes of at least the second graph.

15. The computer program product of claim 9 wherein the one or more user credentials includes one or more of a username, a public key, a private key, a certificate, a creation indicator, an expiration indicator, and a modification indicator.

16. A computing system comprising:

a processor;

a memory module coupled with the processor;

a first software module executable by the processor and the memory module, wherein the first software module is configured to receive one or more data structures from a computing device, wherein the one or more data structures include at least one or more user credentials;

a second software module executable by the processor and the memory module, wherein the second software module is configured to normalize the one or more user credentials of the one or more data structures received from the computing device to generate a first graph;

a third software module executable by the processor and the memory module, wherein the third software module is configured to normalize one or more user credentials of one or more data structures from a storage device to generate a second graph;

a fourth software module executable by the processor and the memory module, wherein the fourth software module is configured to analyze one or more nodes of the first graph and one or more nodes of at least the second graph, wherein analyzing includes at least identifying a logical correlation between the one or more nodes of the first graph and the one or more nodes of at least the second graph;

a fifth software module executable by the processor and the memory module, wherein the fifth software module is configured to generate a third graph based, at least in part, upon the analysis of the one or more nodes of the first graph and the one or more nodes of at least the second graph;

a sixth software module executable by the processor and the memory module, wherein the sixth software module is configured to generate an output data structure based, at least in part, upon the third graph; and

a seventh software module executable by the processor and the memory module, wherein the seventh software module is configured to transmit the output data structure to the computing device.

17. The computing system of claim 16 wherein the first graph is an n-dimensional sparse matrix.

18. The computing system of claim 17 wherein the second software module configured to normalize the one or more user credentials received from the computing device is further configured to:

generate one of the one or more nodes based, at least in part, upon one of the one or more user credentials received from the computing device; and

arrange the one or more nodes, by logical level, into one or more rows of the n-dimensional sparse matrix.

19. The computing system of claim 18 wherein identifying the logical correlation between the one or more nodes of the first graph and the one or more nodes of at least the second graph includes comparing a functionality of the one or more nodes of the first graph and one or more nodes of at least the second graph.

20. The computing system of claim 18 wherein the one or more nodes include subnodes.

21. The computing system of claim 20 wherein the fourth software module configured to generate the third graph is further configured to:

generate one or more nodes and subnodes of the third graph based, at least in part, upon one of the one or more nodes and subnodes of the first graph and the one or more nodes and subnodes of at least the second graph.

22. The computing system of claim 16 wherein the one or more user credentials includes one or more of a username, a public key, a private key, a certificate, a creation indicator, an expiration indicator, and a modification indicator.