PRIVACY-AWARE QUERY MANAGEMENT SYSTEM

• US 20170169253A1
• Filed: 12/10/2015
• Published: 06/15/2017
• Est. Priority Date: 12/10/2015
• Status: Active Grant

• Current Assignee: Neustar Incorporated
• Original Assignee: Neustar Incorporated
• Inventors: Curcio, Matthew, Hollek, Julie, Linde, Steve
• Granted Patent: US 10,108,818 B2
• Time in Patent Office: Days
• CPC Class Codes: G06F 16/24532 of parallel queries
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• 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
• CPC Class Codes: G06F 21/6263 during internet communication, e.g. revealing personal data from cookies

Abstract

A privacy-aware query management system receives a query to aggregate data from a number of datasets, wherein at least one of the datasets contains private information for one or more people. The privacy-aware query management system retrieves data from the datasets. Each of the plurality of datasets has associated privacy parameters that determine a level of noise to be applied to query results obtained from that dataset, and the levels of noise are used to determine an order of operations for the query to retrieve the data from the datasets. The privacy-aware query management system applies the levels of noise to each query result obtained from each of the datasets in accordance with the privacy parameters associated with that dataset, computes aggregated data from the query results with the levels of noise applied, and responds to the query with the aggregated data.

Claims

1. A method of query optimization, the method being implemented by one or more processors and comprising:

receiving a query to aggregate data from a plurality of datasets, wherein at least one of the plurality of datasets contains private information for one or more people;

retrieving data from the plurality of datasets, wherein (1) each of the plurality of datasets has associated privacy parameters that determine a level of noise to be applied to query results obtained from that dataset, and (2) the levels of noise are used to determine an order of operations for the query to retrieve the data from the plurality of datasets;

applying the levels of noise to each query result obtained from each of the plurality of datasets in accordance with the privacy parameters associated with that dataset;

computing aggregated data from the query results with the levels of noise applied; and

responding to the query with the aggregated data.

2. The method of claim 1, wherein the order of operations comprises a query plan constructed to minimize an overall level of noise applied to the aggregated data from the query results.

3. The method of claim 2, further comprising:

receiving a privacy directive along with the query; and

constructing the query plan to optimize the overall level of noise and satisfy the privacy directive.

4. The method of claim 1, wherein the level of noise to be applied for each dataset increases based on each operation performed on that dataset.

5. The method of claim 1, wherein the privacy parameters associated with each dataset include a separate set of privacy parameters for each field in that dataset, and the level of noise to be applied increases based on the separate set of privacy parameters for each field inspected by each operation.

6. The method of claim 1, wherein the privacy parameters include a base level of noise and a privacy budget.

7. The method of claim 6, wherein the base levels of noise and privacy budgets for each dataset are predetermined values chosen to protect privacy for the one or more people.

8. A query optimization system comprising:

a memory resource to store instructions;

one or more processors using the instructions stored in the memory resource to;

receive a query to aggregate data from a plurality of datasets, wherein at least one of the plurality of datasets contains private information for one or more people;

construct a query plan to retrieve data from the plurality of datasets, wherein (1) each of the plurality of datasets has associated privacy parameters that determine a level of noise to be applied to query results obtained from that dataset, and (2) the query plan is constructed to optimize application of the levels of noise;

in response to executing the query plan on the plurality of datasets, apply the levels of noise to each query result obtained from each of the plurality of datasets in accordance with the privacy parameters associated with that dataset;

compute aggregated data from the query results with the levels of noise applied; and

respond to the query with the aggregated data.

9. The system of claim 8, wherein constructing the query plan to optimize application of the levels of noise includes minimizing an overall level of noise applied to the aggregated data from the query results.

10. The system of claim 8, further comprising instructions used by the one or more processors to:

receive a privacy directive along with the query; and

construct the query plan to optimize the level of noise and satisfy the privacy directive.

11. The system of claim 8, wherein the level of noise to be applied for each dataset increases based on each operation performed on that dataset.

12. The system of claim 8, wherein the privacy parameters associated with each dataset include a separate set of privacy parameters for each field in that dataset, and the level of noise to be applied increases based on the separate set of privacy parameters for each field inspected by each operation.

13. The system of claim 8, wherein the privacy parameters include a base level of noise and a privacy budget.

14. The system of claim 13, wherein the base levels of noise and privacy budgets for each dataset are predetermined values chosen to protect privacy for the one or more people.

15. A non-transitory computer-readable medium that stores instructions, executable by one or more processors, to cause the one or more processors to:

receive a query to aggregate data from a plurality of datasets, wherein at least one of the plurality of datasets contains private information for one or more people;

construct a query plan to retrieve data from the plurality of datasets, wherein (1) each of the plurality of datasets has associated privacy parameters that determine a level of noise to be applied to query results obtained from that dataset, and (2) the query plan is constructed to optimize application of the levels of noise;

in response to executing the query plan on the plurality of datasets, apply the levels of noise to each query result obtained from each of the plurality of datasets in accordance with the privacy parameters associated with that dataset;

compute aggregated data from the query results with the levels of noise applied; and

respond to the query with the aggregated data.

16. The non-transitory computer-readable medium of claim 15, wherein constructing the query plan to optimize application of the levels of noise includes minimizing an overall level of noise applied to the aggregated data from the query results.

17. The non-transitory computer-readable medium of claim 15, further comprising instructions used by the one or more processors to:

receive a privacy directive along with the query; and

construct the query plan to optimize the level of noise and satisfy the privacy directive.

18. The non-transitory computer-readable medium of claim 15, wherein the level of noise to be applied for each dataset increases based on each operation performed on that dataset.

19. The non-transitory computer-readable medium of claim 15, wherein the privacy parameters associated with each dataset include a separate set of privacy parameters for each field in that dataset, and the level of noise to be applied increases based on the separate set of privacy parameters for each field inspected by each operation.

20. The non-transitory computer-readable medium of claim 15, wherein the privacy parameters include a base level of noise and a privacy budget.