Apparatus and method for data matching and anonymization

• US 8,978,153 B1
• Filed: 08/01/2014
• Issued: 03/10/2015
• Est. Priority Date: 08/01/2014
• Status: Active Grant

• Current Assignee: Datalogix Incorporated (Oracle Corporation)
• Original Assignee: Datalogix Incorporated (Oracle Corporation)
• Inventors: Cuthbertson, Robert John
• Primary Examiner(s): Rashid, Harunur
• Assistant Examiner(s): Ho, Dao
• Application Number:
  US14/449,831
• Time in Patent Office: 221 Days
• Field of Search: 726/26
• US Class Current: 726/26
• CPC Class Codes: G06F 21/602 Providing cryptographic facilities or services
• CPC Class Codes: G06F 21/6254 by anonymising data, e.g. decorrelating personal data from the owner's identification
• CPC Class Codes: H04L 2209/42 Anonymization, e.g. involving pseudonyms
• CPC Class Codes: H04L 63/0407 wherein the identity of one or more communicating identities is hidden cryptographic mechanisms or cryptographic arrangements for anonymous credentials or for identity based cryptographic systems H04L9/00
• CPC Class Codes: H04L 67/306 User profiles
• CPC Class Codes: H04L 67/535 Tracking the activity of the user network monitoring arrangements H04L43/00; recording of computer activity G06F11/34
• CPC Class Codes: H04L 9/0894 Escrow, recovery or storing of secret information, e.g. secret key escrow or cryptographic key storage

Abstract

A method includes receiving a plurality of data sets. Each data set includes a customer identifier field specifying a unique customer identifier associated with each entry in each data set. The plurality of data sets includes a first group of data sets and a second group of data sets. The method further includes storing the plurality of data sets, and generating a key map including the customer identifier field including unique customer identifiers of the first group of data sets of the plurality of data sets, and an anonymous identifier field including unique anonymous identifiers. Each anonymous identifier corresponds to a customer identifier of the key map. The method further includes replacing each unique customer identifier in the second group of data sets with the corresponding anonymous identifier.

Claims

1. A method, comprising:

receiving a plurality of data sets, each data set including a customer identifier field, the customer identifier field specifying a unique customer identifier associated with each entry in each data set, the plurality of data sets including a first group of data sets and a second group of data sets, each data set associable with each other data set based on the customer identifier;

storing the plurality of data sets;

generating a key map including the customer identifier field and an anonymous identifier field, the customer identifier field for the key map including unique customer identifiers of the first group of data sets of the plurality of data sets, the anonymous identifier field including unique anonymous identifiers, each anonymous identifier corresponding to a customer identifier of the key map, each anonymous identifier different from its corresponding customer identifier;

storing the key map;

rendering each data set of the second group of data sets as unassociable with each data set of the first group of data sets by replacing, based on the key map, each unique customer identifier in the second group of data sets with the corresponding anonymous identifier, further including rendering each data set of the second group of data sets associable with each other data set of the second group of data sets based on the anonymous identifier; and

deleting the key map.

2. The method of claim 1, wherein the key map is stored in a different location than the plurality of data sets.

3. The method of claim 1, further comprising correlating, without employing the first group of data sets or the key map, a first entry in a first data set of the second group of data sets with a second entry in a second data set of the second group of data sets based on the anonymous identifier associated with the first entry.

4. The method of claim 1, further comprising encrypting the key map with an encryption scheme selected from the following:

a public-key encryption scheme and a symmetric key encryption scheme.

5. The method of claim 1, wherein the first group of data sets include demographic information for a plurality of households, and the second group of data sets includes transaction information for the plurality of households.

6. A method, comprising:

receiving a reference data set including a customer identifier field, the customer identifier field of the reference data set specifying a unique customer identifier associated with each entry in the reference data set;

receiving a first data set including the customer identifier field, the customer identifier field for the first data set specifying a unique customer identifier associated with each entry in the first data set, the first data set associable with the reference data set based on the customer identifier field;

storing the reference data set and the first data set;

rendering the first data set unassociable with the reference data set by replacing each unique customer identifier of the second data set with a corresponding anonymous identifier based on a key map, the key map including the customer identifier field and an anonymous identifier field, the customer identifier field for the key map including the unique customer identifiers of the second data set, the anonymous identifier field including unique anonymous identifiers, each anonymous identifier corresponding to a customer identifier of the key map, each anonymous identifier different from its corresponding customer identifier;

receiving one or more additional data sets, each additional data set including the customer identifier field, the one or more additional data sets associable with the reference data set based on the customer identifier field and unassociable with the first data set;

rendering the one or more additional data sets as unassociable with the reference data set by replacing each unique customer identifier in each additional data set with its corresponding anonymous identifier, based on the key map, the rendering including rendering the one or more additional data sets as associable with the first data set based on the anonymous identifier field; and

deleting the key map.

7. The method of claim 6, wherein the key map is stored in a different location than the reference data set and the first data set.

8. The method of claim 6, further comprising correlating a first entry in the reference data set with a second entry in the first data set, including:

identifying, in the reference data set, the customer identifier associated with the first entry;

identifying, in the key map, the anonymous identifier corresponding to the identified first identifier;

identifying the second entry in the first data set corresponding to the identified anonymous identifier; and

returning the second entry in the first data set as correlated to the first entry in the reference data set.

9. The method of claim 6, further comprising encrypting the key map with an encryption scheme selected from the following:

a public-key encryption scheme and a symmetric key encryption scheme.

10. The method of claim 6, wherein the reference data set includes demographic information for a plurality of households, and at least one of the first data set and the additional data sets includes transaction information for the plurality of households.

11. The method of claim 6, further comprising:

correlating, without employing the reference data set or the key map, a first entry in the first data set with a second entry in one of the additional data sets based on the anonymous identifier associated with the first entry.

12. The method of claim 6, further comprising:

generating the key map including the customer identifier field and the anonymous identifier field; and

storing the key map in a different location than the reference data set, the first data sets, and the one or more additional data sets.

13. An apparatus, comprising:

a processor operatively coupled to a memory, the processor configured to execute a data module and a key module;

the data module configured to;

receive a plurality of data sets, each data set including a customer identifier field, the customer identifier field specifying a unique customer identifier associated with each entry in each data set, the plurality of data sets including a first group of data sets and a second group of data sets, each data set associable with each other data set based on the customer identifier; and

store the plurality of data sets; and

the key module configured to generate a key map including the customer identifier field and an anonymous identifier field, the customer identifier field for the key map including unique customer identifiers of the first group of data sets of the plurality of data sets, the anonymous identifier field including unique anonymous identifiers, each anonymous identifier corresponding to a customer identifier of the key map, each anonymous identifier different from its corresponding customer identifier,

the data module further configured to store the key map,

the key module further configured to render each data set of the second group of data sets as unassociable with each data set of the first group of data sets by replacing, based on the key map, each unique customer identifier in the second group of data sets with the corresponding anonymous identifier, including rendering each data set of the second group of data sets associable with each other data set of the second group of data sets based on the anonymous identifier, and

the data module further configured to delete the key map.

14. The apparatus of claim 13, wherein the data module is further configured to store the key map in a different location than the first group of data sets and the second group of data sets.

15. The apparatus of claim 13, wherein the data module is further configured to correlate a first entry in a first data set of the second group of data sets with a second entry in a second data set of the second group of data sets by:

identifying, in the first data set, the anonymous identifier associated with the first entry;

identifying the second entry in the second data set corresponding to the identified anonymous identifier; and

returning the second entry in the second data set as correlated to the first entry in the first data set.

16. The apparatus of claim 13, further comprising an encryption module configured to encrypt the key map with an encryption scheme selected from the following:

a public-key encryption scheme and a symmetric key encryption scheme.

17. The apparatus of claim 13, wherein the first group of data sets include demographic information for a plurality of households, and second group of data sets include transaction information for the plurality of households.