Differentially private processing and database storage

• US 10,733,320 B2
• Filed: 01/02/2019
• Issued: 08/04/2020
• Est. Priority Date: 11/02/2015
• Status: Active Grant

• Current Assignee: Snowflake, Inc.
• Original Assignee: LeapYear Technologies, Inc. (Snowflake Inc)
• Inventors: Nerurkar, Ishaan, Hockenbrocht, Christopher, Damewood, Liam, Maruseac, Mihai, Rozenshteyn, Alexander
• Primary Examiner(s): Leung, Robert B
• Application Number:
  US16/238,439
• Publication Number:
  US 20190141052A1
• Time in Patent Office: 580 Days
• CPC Class Codes: G06F 16/24547 Optimisations to support specific applications; Extensibility of optimisers
• CPC Class Codes: G06F 16/2455 Query execution
• CPC Class Codes: G06F 16/2462 Approximate or statistical queries
• CPC Class Codes: G06F 16/2465 Query processing support for facilitating data mining operations in structured databases
• CPC Class Codes: G06F 16/248 Presentation of query results
• CPC Class Codes: G06F 16/25 Integrating or interfacing systems involving database management systems
• CPC Class Codes: G06F 21/6218 to a system of files or objects, e.g. local or distributed file system or database
• CPC Class Codes: G06F 21/6227 where protection concerns the structure of data, e.g. records, types, queries
• 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: G06N 20/00 Machine learning
• CPC Class Codes: G06N 20/20 Ensemble learning
• CPC Class Codes: G06N 5/01 Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
• CPC Class Codes: H04L 63/105 Multiple levels of security

Abstract

A hardware database privacy device is communicatively coupled to a private database system. The hardware database privacy device receives a request from a client device to perform a query of the private database system and identifies a level of differential privacy corresponding to the request. The identified level of differential privacy includes privacy parameters (ε,δ) indicating the degree of information released about the private database system. The hardware database privacy device identifies a set of operations to be performed on the set of data that corresponds to the requested query. After the set of data is accessed, the set of operations is modified based on the identified level of differential privacy such that a performance of the modified set of operations produces a result set that is (ε,δ)-differentially private.

Claims

1. A method for returning differentially private results in response to a query to a database storing restricted data as records comprising rows and columns, the method comprising:

receiving a database query from a client device, the database query requesting a random forest classifier correlating values of columns in a set of records in the database with category labels associated with the rows, the database query identifying a level of differential privacy corresponding to the request, the identified level of differential privacy comprising privacy parameters ε and

δ

, wherein ε

describes a degree of information released about the restricted data stored in the database due to the query, and δ

describes an improbability of the query satisfying (ε

)-differential privacy;

performing the database query on the set of records to produce a differentially private version of the random forest classifier that maintains the identified level of differential privacy for the restricted data, performing the query comprising;

training the random forest classifier upon the values of columns in the set of records and the category labels of the rows, wherein the random forest classifier comprises a set of decision trees, each decision tree having one or more leaf nodes, and each leaf node indicating a relative proportion of rows labeled with each category in the leaf node; and

producing a differentially private version of the random forest classifier by perturbing relative proportions of rows labeled with each category in each leaf node by;

2. The method of claim 1, wherein the client device is used by a user and further comprising:

identifying a privacy budget associated with the user, the privacy budget representing remaining queries available to the user in terms of the privacy parameters ε and

δ

;

decrementing the privacy budget associated with the user responsive to performing the database query and based on the privacy parameters corresponding to the request to produce a decremented privacy budget.

3. The method of claim 2, further comprising:

receiving a subsequent query of the database from the user, the subsequent query identifying a corresponding level of differential privacy;

checking whether the corresponding level of differential privacy can be accommodated by the decremented privacy budget; and

denying the subsequent query responsive to the check indicating that the corresponding level of differential privacy cannot be accommodated by the decremented privacy budget.

4. The method of claim 1, wherein the privacy budget is specified by an entity managing the database.

5. The method of claim 1, wherein the random forest classifier comprises an ensemble of binary decision trees and N_trees indicates a number of decision trees in the ensemble.

6. The method of claim 1, wherein the random forest classifier comprises an ensemble of binary decision trees each having leaf nodes, and producing the differentially private version of the random forest classifier comprises perturbing the leaf nodes of the ensemble of binary decision trees.

7. The method of claim 1, wherein the query specifies a subset of the columns in the database to use to generate the random forest classifier, and wherein the random forest classifier is trained upon the values of the specified subset of columns.

8. A non-transitory computer-readable storage medium storing computer program instructions executable by a processor to perform operations for returning differentially private results in response to a query to a database storing restricted data as records comprising rows and columns, the operations comprising:

receiving a database query from a client device, the database query requesting a random forest classifier correlating values of columns in a set of records in the database with category labels associated with the rows, the database query identifying a level of differential privacy corresponding to the request, the identified level of differential privacy comprising privacy parameters ε and

δ

, wherein ε

describes a degree of information released about the restricted data stored in the database due to the query, and δ

describes an improbability of the query satisfying (ε

)-differential privacy;

performing the database query on the set of records to produce a differentially private version of the random forest classifier that maintains the identified level of differential privacy for the restricted data, performing the query comprising;

training the random forest classifier upon the values of columns in the set of records and the category labels of the rows, wherein the random forest classifier comprises a set of decision trees, each decision tree having one or more leaf nodes, and each leaf node indicating a relative proportion of rows labeled with each category in the leaf node; and

producing a differentially private version of the random forest classifier by perturbing relative proportions of rows labeled with each category in each leaf node by;

9. The non-transitory computer-readable storage medium of claim 8, wherein the client device is used by a user, the operations further comprising:

identifying a privacy budget associated with the user, the privacy budget representing remaining queries available to the user in terms of the privacy parameters ε and

δ

;

decrementing the privacy budget associated with the user responsive to performing the database query and based on the privacy parameters corresponding to the request to produce a decremented privacy budget.

10. The non-transitory computer-readable storage medium of claim 9, the operations further comprising:

receiving a subsequent query of the database from the user, the subsequent query identifying a corresponding level of differential privacy;

checking whether the corresponding level of differential privacy can be accommodated by the decremented privacy budget; and

denying the subsequent query responsive to the check indicating that the corresponding level of differential privacy cannot be accommodated by the decremented privacy budget.

11. The non-transitory computer-readable storage medium of claim 8, wherein the privacy budget is specified by an entity managing the database.

12. The non-transitory computer-readable storage medium of claim 8, wherein the random forest classifier comprises an ensemble of binary decision trees and N_trees indicates a number of decision trees in the ensemble.

13. The non-transitory computer-readable storage medium of claim 8, wherein the random forest classifier comprises an ensemble of binary decision trees each having leaf nodes, and producing the differentially private version of the random forest classifier comprises perturbing the leaf nodes of the ensemble of binary decision trees.

14. The non-transitory computer-readable storage medium of claim 8, wherein the query specifies a subset of the columns in the database to use to generate the random forest classifier, and wherein the random forest classifier is trained upon the values of the specified subset of columns.

15. A system, comprising:

a processor for executing computer program instructions; and

a non-transitory computer-readable storage medium storing computer program instructions executable by the processor to perform operations for returning differentially private results in response to a query to a database storing restricted data as records comprising rows and columns, the operations comprising;

receiving a database query from a client device, the database query requesting a random forest classifier correlating values of columns in a set of records in the database with category labels associated with the rows, the database query identifying a level of differential privacy corresponding to the request, the identified level of differential privacy comprising privacy parameters ε and

δ

, wherein ε

describes a degree of information released about the restricted data stored in the database due to the query, and δ

describes an improbability of the query satisfying (ε

)-differential privacy;

performing the database query on the set of records to produce a differentially private version of the random forest classifier that maintains the identified level of differential privacy for the restricted data, performing the query comprising;

training the random forest classifier upon the values of columns in the set of records and the category labels of the rows, wherein the random forest classifier comprises a set of decision trees, each decision tree having one or more leaf nodes, and each leaf node indicating a relative proportion of rows labeled with each category in the leaf node; and

producing a differentially private version of the random forest classifier by perturbing relative proportions of rows labeled with each category in each leaf node by;

16. The system of claim 15, wherein the client device is used by a user, the operations further comprising:

identifying a privacy budget associated with the user, the privacy budget representing remaining queries available to the user in terms of the privacy parameters ε and

δ

;

decrementing the privacy budget associated with the user responsive to performing the database query and based on the privacy parameters corresponding to the request to produce a decremented privacy budget.

17. The system of claim 16, the operations further comprising:

receiving a subsequent query of the database from the user, the subsequent query identifying a corresponding level of differential privacy;

checking whether the corresponding level of differential privacy can be accommodated by the decremented privacy budget; and

denying the subsequent query responsive to the check indicating that the corresponding level of differential privacy cannot be accommodated by the decremented privacy budget.

18. The system of claim 15, wherein the random forest classifier comprises an ensemble of binary decision trees and N_trees indicates a number of decision trees in the ensemble.

19. The system of claim 15, wherein the random forest classifier comprises an ensemble of binary decision trees each having leaf nodes, and producing the differentially private version of the random forest classifier comprises perturbing the leaf nodes of the ensemble of binary decision trees.

20. The system of claim 15, wherein the query specifies a subset of the columns in the database to use to generate the random forest classifier, and wherein the random forest classifier is trained upon the values of the specified subset of columns.