System and Method for Protecting User Privacy Using Social Inference Protection Techniques

• US 20100024042A1
• Filed: 07/22/2009
• Published: 01/28/2010
• Est. Priority Date: 07/22/2008
• Status: Active Grant

• Current Assignee: New Jersey Institute of Technology
• Original Assignee: New Jersey Institute of Technology
• Inventors: Motahari, Sara Gatmir, Jones, Quentin
• Granted Patent: US 8,504,481 B2
• Time in Patent Office: Days
• US Class Current: 726/26
• CPC Class Codes: G06F 21/577 Assessing vulnerabilities and evaluating computer system security
• CPC Class Codes: G06F 21/6263 during internet communication, e.g. revealing personal data from cookies
• CPC Class Codes: G06N 5/02 Knowledge representation; Symbolic representation
• CPC Class Codes: G06Q 10/10 Office automation; Time management
• CPC Class Codes: H04L 63/0421 Anonymous communication, i.e. the party's identifiers are hidden from the other party or parties, e.g. using an anonymizer
• CPC Class Codes: H04W 12/02 Protecting privacy or anonymity, e.g. protecting personally identifiable information [PII]

Abstract

A system and method for protecting user privacy using social inference protection techniques is provided. The system executes a plurality of software modules which model of background knowledge associated with one or more users of the mobile computing devices; estimate information entropy of a user attribute which could include identity, location, profile information, etc.; utilize the information entropy models to predict the social inference risk; and minimize privacy risks by taking a protective action after detecting a high risk.

Claims

1. A system for protecting individual privacy in a computer network, comprising:

first means for modeling a context associated with an individual user and storing the modeled context in a data store;

second means for calculating an information entropy level associated with a user and storing the calculated information entropy level in the data store;

third means for calculating a privacy threshold associated with a user and storing the calculated privacy threshold in the data store; and

fourth means for executing at least one privacy protection action based upon the modeled context, the calculated information entropy level, and the calculated privacy threshold.

2. The system of claim 1, wherein the first means implements a deterministic model of background information associated with a user.

3. The system of claim 1, wherein the first means implements a probabilistic model of background information associated with a user.

4. The system of claim 1, wherein the first means models vicinity information about a user'"'"'s vicinity.

5. The system of claim 4, wherein the modeled vicinity information includes at least one of names of nearby persons, profiles of nearby persons, and information about nearby locations.

6. The system of claim 1, wherein the first means models personal information about people nearby a user.

7. The system of claim 6, wherein the personal information includes at least one of a user'"'"'s demographic information, publicly-available information about people, gender information, ethnicity information, geotemporal routines, and individual interests/attributes.

8. The system of claim 1, wherein the second means implements an instantaneous entropy model.

9. The system of claim 8, wherein the instantaneous entropy model models instantaneous information entropy.

10. The system of claim 8, wherein the instantaneous entropy model models instantaneous identity entropy.

11. The system of claim 1, wherein the second means implements a historical entropy model.

12. The system of claim 11, wherein the historical entropy model models historical information entropy.

13. The system of claim 11, wherein the historical entropy model models historical identity entropy.

14. The system of claim 1, wherein the third means determines and stores information about at least one of privacy preferences, anonymity preferences, group privacy preferences, system administrator settings, legal requirements, or social customs.

15. The system of claim 1, wherein the privacy protection action implemented by the fourth means includes at least one of blurring an answer to a user query for information, rejecting an answer to a user query for information, alerting a user as to a privacy risk, informing the user about a current entropy level, informing the user about a history of revealed information, reminding the user about current privacy settings, adjusting the user'"'"'s privacy settings, and adjusting system administration policy settings.

16. The system of claim 1, wherein the second means implements an inference function of

17. The system of claim 16, wherein the second means implements an inference function of

18. A method for protecting individual privacy in a computer network, comprising the steps of:

providing a computing device in communication with a network;

modeling a context associated with a user of the at least one computing device and storing the modeled context in a data store;

calculating an information entropy level associated with the user using the computing device;

storing the calculated information entropy level in a data store of the computing device;

calculating a privacy threshold associated with the user using the computing device;

storing the calculated privacy threshold in the data store of the computing device; and

implementing at least one privacy protection action based upon the modeled context, the calculated information entropy level, and the calculated privacy threshold.

19. The method of claim 18, wherein the step of modeling the context comprises implementing a deterministic model of background information associated with the user.

20. The method of claim 18, wherein the step of modeling the context comprises implementing a probabilistic model of background information associated with the user.

22. The method of claim 18, wherein the step of modeling the context comprises modeling personal information about people nearby the user including at least one of user demographic information, publicly-available information about the people, gender information, and ethnicity information.

23. The method of claim 18, wherein the step of calculating the information entropy level comprises implementing an instantaneous entropy model which models at least one of instantaneous information entropy and instantaneous identity entropy.

24. The method of claim 18, wherein the step of calculating the information entropy level comprises implementing a historical entropy model which models at least one of historical information entropy and historical identity entropy.

25. The method of claim 18, wherein the step of calculating the information entropy level comprises determining and storing information about at least one of privacy preferences, anonymity preferences, group privacy preferences, system administrator settings, legal requirements, or social customs.

26. The method of claim 18, wherein the step of implementing the at least one privacy protection action includes at least one of blurring an answer to a user query for information, rejecting an answer to a user query for information, alerting a user as to a privacy risk, informing the user about a current entropy level, informing the user about a history of revealed information, reminding the user about current privacy settings, adjusting the user'"'"'s privacy settings, and adjusting system administration policy settings.

21. The method of claim 21, wherein the step of modeling the context comprises modeling vicinity information about the user'"'"'s vicinity including at least one of names of nearby persons, profiles of nearby persons, and information about nearby locations.