Apparatus and Methods For Anonymizing a Data Set

• US 20140172854A1
• Filed: 12/17/2012
• Published: 06/19/2014
• Est. Priority Date: 12/17/2012
• Status: Active Grant

• Current Assignee: Telefonaktiebolaget LM Ericsson
• Original Assignee: Telefonaktiebolaget LM Ericsson
• Inventors: HUANG, Vincent, Svensson, Martin
• Granted Patent: US 9,202,081 B2
• Time in Patent Office: Days
• US Class Current: 707/737
• CPC Class Codes: G06F 16/285 Clustering or classification
• CPC Class Codes: G06F 16/288 Entity relationship models
• CPC Class Codes: G06F 21/6245 Protecting personal data, e.g. for financial or medical purposes
• CPC Class Codes: G06F 21/6254 by anonymising data, e.g. decorrelating personal data from the owner's identification

Abstract

Methods and systems are disclosed for anonymizing a dataset that correlates a set of entities with respective attributes. The method may include: for each entity included in a set of entities, transforming two or more attribute values associated with the entity using received preference information, thereby creating for the entity a set of two or more transformed attribute values; clustering the entities included in the set of entities using said transformed attribute values to form at least a first entity cluster consisting of a first subset of the entities and a second entity cluster consisting of a second subset of the entities, wherein no entity included in the first entity cluster is included in the second entity cluster; anonymizing the first subset of entities; and anonymizing the second subset of entities.

Claims

1. A computerized method for anonymizing a dataset that associates each entity included in a set of entities with a set of two or more attribute values, wherein each said attribute value associated with the entity is associated with a different entity attribute, the method being performed by a computer system and comprising:

receiving preference information;

for each entity included in the set of entities, transforming said set of two or more attribute values associated with the entity using the preference information, thereby creating for the entity a set of two or more transformed attribute values;

clustering the entities included in the set of entities using said transformed attribute values to form at least a first entity cluster consisting of a first subset of the entities and a second entity cluster consisting of a second subset of the entities, wherein no entity included in the first entity cluster is included in the second entity cluster;

anonymizing the first subset of entities; and

anonymizing the second subset of entities.

2. The method of claim 1, wherein

the preference information comprises a set attribute weights, and

each attribute weight included in the set is associated with one of the entity attributes.

3. The method of claim 2, wherein the step of transforming the attribute values associated with a particular one of the entities comprises:

normalizing the attribute values, thereby producing normalized attribute values; and

for each normalized attribute value, calculating a transformed value using the normalized attribute value and the weight associated with the entity attribute with which the normalized attribute is associated.

4. The method of claim 3, wherein the step of calculating the transformed value comprises multiplying the weight with a value that is equal to the normalized value minus a first value and adding the result of the multiplication with the first value.

5. The method of claim 4, wherein the first value is the mean of a set of values.

6. The method of claim 1, wherein

the step of anonymizing the first subset of entities comprises creating a first new anonymous dataset that:

(1) associates a first common attribute value with each entity included the first subset, the first common attribute value being associated with a first entity attribute and (2) associates a second common attribute value with each entity included the first subset, the second common attribute value being associated with a second entity attribute that is different than the first entity attribute, and

the step of anonymizing the second subset of entities comprises creating a second new anonymous dataset that;

(1) associates a third common attribute value with each entity included the second subset, the third common attribute value being associated with the first entity attribute and (2) associates a fourth common attribute value with each entity included the second subset, the fourth common attribute value being associated with the second entity attribute.

7. The method of claim 6, further comprising publishing the first and second new anonymous datasets.

8. The method of claim 1, wherein the step of clustering the entities comprises:

creating an intermediate dataset using the transformed attribute values and processing the intermediate dataset to create an unsparse dataset that is less sparse than the intermediate dataset; and

identifying entity clusters using the unsparse dataset.

9. A database anonymizing apparatus, comprising

a data processing system;

and

a data storage system, wherein

the data storage system stores a dataset that associates each entity included in a set of entities with a set of two or more attribute values, wherein each said attribute value associated with the entity is associated with a different entity attribute,

the data storage system comprises a computer readable medium storing computer readable program code (CRPC), and

the CRPC comprises;

instructions for, for each entity included in the set of entities, transforming said set of two or more attribute values associated with the entity using received preference information, thereby creating for the entity a set of two or more transformed attribute values;

instructions for clustering the entities included in the set of entities using said transformed attribute values to form at least a first entity cluster consisting of a first subset of the entities and a second entity cluster consisting of a second subset of the entities, wherein no entity included in the first entity cluster is included in the second entity cluster;

instructions for anonymizing the first subset of entities; and

instructions for anonymizing the second subset of entities.

10. The database anonymizing apparatus of claim 9, wherein

the preference information comprises a set attribute weights, and

each attribute weight included in the set is associated with one of the entity attributes.

11. The database anonymizing apparatus of claim 10, wherein the instructions for transforming the attribute values comprises:

instructions for normalizing the attribute values, thereby producing normalized attribute values; and

instructions for, for each normalized attribute value, calculating a transformed value using the normalized attribute value and the weight associated with the entity attribute with which the normalized attribute is associated.

12. The database anonymizing apparatus of claim 11, wherein the instructions for calculating the transformed value comprises instructions for multiplying the weight with a value that is equal to the normalized value minus a first value and instructions for adding the result of the multiplication with the first value.

13. The database anonymizing apparatus of claim 12, wherein the first value is the mean of a set of values.

14. The database anonymizing apparatus of claim 9, wherein

the instructions for anonymizing the first subset of entities comprises instructions for creating a first new anonymous dataset that:

(1) associates a first common attribute value with each entity included the first subset, the first common attribute value being associated with a first entity attribute and (2) associates a second common attribute value with each entity included the first subset, the second common attribute value being associated with a second entity attribute that is different than the first entity attribute, and

the instructions for anonymizing the second subset of entities comprises instructions for creating a second new anonymous dataset that;

(1) associates a third common attribute value with each entity included the second subset, the third common attribute value being associated with the first entity attribute and (2) associates a fourth common attribute value with each entity included the second subset, the fourth common attribute value being associated with the second entity attribute.

15. The database anonymizing apparatus of claim 14, wherein the CRPC further comprises instructions for publishing the first and second new anonymous datasets.

16. The database anonymizing apparatus of claim 9, wherein the instructions for clustering the entities comprises:

instructions for creating an intermediate dataset using the transformed attribute values and processing the intermediate dataset to create an unsparse dataset that is less sparse than the intermediate dataset; and

instructions for identifying entity clusters using the unsparse dataset.