SYSTEMS AND METHODS FOR ANONYMIZED USER LIST COUNT

• US 20150293923A1
• Filed: 03/24/2015
• Published: 10/15/2015
• Est. Priority Date: 04/10/2014
• Status: Abandoned Application

• Current Assignee: Max-Planck-Gesellschaft Zur Foerderung Der Wissenschaften e.V. (Max-Planck-Gesellschaft zur Frderung der Wissenschaften e)
• Original Assignee: Max-Planck-Gesellschaft Zur Foerderung Der Wissenschaften e.V. (Max-Planck-Gesellschaft zur Frderung der Wissenschaften e)
• Inventors: BERNEANU, CRISTIAN DANIEL, EIDE, SEBASTIAN PROBST, FRANCIS, PAUL, BAUER, FELIX, KRETSCHMER, MATTHIAS, JURIC, SASA
• Application Number:
  US14/666,937
• Publication Number:
  US 20150293923A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/2228 Indexing structures
• CPC Class Codes: G06F 16/24578 using ranking
• CPC Class Codes: G06F 16/2462 Approximate or statistical queries
• CPC Class Codes: G06F 21/6254 by anonymising data, e.g. decorrelating personal data from the owner's identification

Abstract

A computer system includes a database configured to receive a query and to produce a list of User IDs and an anonymization module. The anonymization module is configured to receive a list of user IDs in response to a query, the list of user IDs defining a true user count, generate a noisy user count of the list of user IDs, compare the true user count to a first threshold value stored in memory, compare the noisy user count to a second threshold value stored in memory, and output the noisy user count only if the true user count is greater than the first threshold value and the noisy user count is greater then the second threshold.

Claims

1. An anonymizing method for a database system, comprising the steps of:

receiving a list of user IDs in response to a query, the list of user IDs defining a true user count;

generating a noisy user count of the list of user IDs;

comparing the true user count to a first threshold value stored in memory;

comparing the noisy user count to a second threshold value stored in memory;

outputting the noisy user count only if the true user count is greater than the first threshold value and the noisy user count is greater then the second threshold.

2. The method according to claim 1, further comprising the step of:

if the noisy user count is not output, outputting a message indicating that the noisy user count is too small to report.

3. The method according to claim 1, wherein:

the first threshold value is less than the second threshold value.

4. The method according to claim 1, further comprising the step of:

comparing the true user count to a number of queried users less the first threshold value; and

if the true user count is greater than the number of queried users less the first threshold value, outputting a message that the noisy user count is too large to report.

5. The method according to claim 4, further comprising the step of:

comparing the noisy user count to the number of queried users less the second threshold value; and

if the noisy user count is greater than the number of queried users less the second threshold value, outputting a message that the noisy user count is too large to report.

6. The method according to claim 1, further comprising the step of:

increasing an amount of noise added to the true user count in dependence upon a magnitude of the true user count.

7. The method according to claim 1, wherein:

the steps are performed by an anonymization module communicatively coupled to a database.

8. The method according to claim 1, wherein:

the step of generating the noisy user count includes adding layered noise to the true user count; and

wherein the layered noise includes a plurality of noise values that are added to the true user count and are varied in dependence upon a user list count, the user list count representing a number of user lists that have been provided.

9. An anonymizing method for a database system, comprising the steps of:

receiving a list of user IDs in response to a query, the list of user IDs defining a true user count;

generating a noisy user count by adding layered noise to the true user count, the layered noise including a plurality of noise values that are added to the true user count and are varied in dependence upon a user list count, the user list count representing a number of user lists that have been provided; and

outputting the noisy user count.

10. The method according to claim 9, wherein:

the steps are performed by an anonymization module communicatively coupled to a database.

11. An anonymizing method for a database system, comprising the steps of:

receiving a new list of user IDs in response to a new query;

comparing the new list with at least one stored list to determine a new user count, the new user count being a number of users that are in the new list but not the stored list;

generating a noisy difference value by adding noise to the new user count;

comparing the noisy difference value to a first threshold value stored in memory;

outputting the noisy count corresponding to the stored list if the noisy difference value is less than the first threshold value; and

outputting a new noisy count for the new list if the noisy difference value is greater than the first threshold value.

12. The method according to claim 11, further comprising the steps of:

receiving a plurality of lists of user IDs; and

storing at least one of plurality of lists of user IDs as the at least one stored list.

13. The method according to claim 12, further comprising the step of:

generating a noisy count for each of the stored lists; and

storing the noisy count for each of the stored lists.

14. The method according to claim 13, wherein:

the first threshold value is chosen based upon the size of the at least one stored list.

15. The method according to claim 13, wherein:

the new list is only compared with the at least one stored list if the noisy difference and the noisy count for the at least one stored list are within a predetermined value of each other.

16. The method according to claim 12, wherein:

the at least one stored list is a condensed stored list; and

the method includes, after receiving the new list, condensing the new list into a condensed new list and comparing the condensed new list with the condensed stored list.

17. The method according to claim 16, further comprising the step of:

determining whether the condensed new list and the condensed stored lists are matching lists; and

if the condensed new list and the condensed stored list are matching lists, outputting for the new list a noisy count of the stored list that corresponds to the matching condensed stored list.

18. The method according to claim 17, wherein:

the condensed stored list is a single value.

19. The method according to claim 11, wherein:

the steps are performed by an anonymization module communicatively coupled to a database.

20. The method according to claim 19, wherein:

the at least one stored list is stored within the anonymization module.

21. The method according to claim 11, wherein:

the new list is a plurality of new lists received in response to the query; and

wherein the method includes the steps of determining the number of lists in which a user belongs, and if the user belongs in more than a threshold number of lists, removing the user from all but the threshold number of lists.

22. An anonymizing method for a database system, comprising the steps of:

in response to a plurality of queries, receiving a plurality of answers, each answer including a list of users defining a true user count for each answer;

storing a frequency with which each user appears in the answers;

determining if any of the users are high-touch users; and

removing at least one of the high-touch users from at least one of the answers to reduce the true user count for the at least one of the answers.

23. The method according to claim 22, wherein:

the step of removing at least one of the high-touch users includes removing a random number of high-touch users.

24. The method according to claim 22, wherein:

the step of removing at least one of the high-touch users includes removing a number of high-touch users up to a predetermined threshold.

25. The method according to claim 22, wherein:

the step of determining if any of the users are high-touch users includes determining a probability of a user'"'"'s appearance in the answers relative to an average number of appearances.