Differentially private processing and database storage

• US 10,242,224 B2
• Filed: 10/25/2017
• Issued: 03/26/2019
• 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
• Primary Examiner(s): Leung, Robert B
• Application Number:
  US15/793,898
• Publication Number:
  US 20180048653A1
• Time in Patent Office: 517 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 health data for a plurality of patients, the database storing records comprising rows and columns, where the rows are associated with patients having a medical condition, and columns of the rows contain values describing health data for the patients, the method comprising:

receiving a database query from a client device, the database query requesting a linear model describing correlations among values of columns in a set of records in the database, the database query specifying a degree of privacy to maintain for the restricted data;

performing the database query on the set of records in the database to produce a differentially private version of the linear model that maintains the specified degree of privacy for the restricted data, performing the query comprising;

modeling the values of the columns in the set of records in the database to produce a linear model comprising a set of parameters θ

that describe correlations among the values describing health data for the patients;

minimizing a loss function on the linear model over possible values of the set of parameters θ

to produce an optimal set of parameters θ

; and

perturbing the optimal set of parameters θ

to produce the differentially private version of the linear model, the set of parameters θ

perturbed by a factor defined by;

2. The method of claim 1, wherein the client device is associated with a privacy budget representing remaining queries available to the client device in terms of at least one of ε

and δ

, the method further comprising;

determining whether decrementing the privacy budget by the specified degree of privacy exceeds the remaining queries available, wherein performing the database query is responsive to determining that decrementing the privacy budget by the specified degree of privacy does not exceed the remaining queries available; and

decrementing the privacy budget by the specified degree of privacy.

3. The method of claim 2, wherein the privacy budget is specified by an entity managing the database.

4. The method of claim 1, wherein the linear model is at least one of a linear regression classifier, a logistic regression classifier, and a support vector machine classifier.

5. The method of claim 1, wherein K is the Lipschitz constant.

6. The method of claim 1, wherein minimizing the loss function on the linear model over possible values of the set of parameters θ

to produce an optimal set of parameters θ

comprises;

minimizing according to

7. The method of claim 1, wherein each column in the set of records represents one of a label feature and an observable feature describing health data of the patients.

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 health data for a plurality of patients, the database storing records comprising rows and columns, where the rows are associated with patients having a medical condition, and columns of the rows contain values describing health data for the patients, the operations comprising:

receiving a database query from a client device, the database query requesting a linear model describing correlations among values of columns in a set of records in the database, the database query specifying a degree of privacy to maintain for the restricted data;

performing the database query on the set of records in the database to produce a differentially private version of the linear model that maintains the specified degree of privacy for the restricted data, performing the query comprising;

modeling the values of the columns in the set of records in the database to produce a linear model comprising a set of parameters θ

that describe correlations among the values describing health data for the patients;

minimizing a loss function on the linear model over possible values of the set of parameters θ

to produce an optimal set of parameters θ

; and

perturbing the optimal set of parameters θ

to produce the differentially private version of the linear model, the set of parameters θ

perturbed by a factor defined by;

9. The non-transitory computer-readable storage medium of claim 8, wherein the client device is associated with a privacy budget representing remaining queries available to the client device in terms of at least one of c and 6, the operations further comprising:

determining whether decrementing the privacy budget by the specified degree of privacy exceeds the remaining queries available, wherein performing the database query is responsive to determining that decrementing the privacy budget by the specified degree of privacy does not exceed the remaining queries available; and

decrementing the privacy budget by the specified degree of privacy.

10. The non-transitory computer-readable storage medium of claim 9, wherein the privacy budget is specified by an entity managing the database.

11. The non-transitory computer-readable storage medium of claim 8, wherein the linear model is at least one of a linear regression classifier, a logistic regression classifier, and a support vector machine classifier.

12. The non-transitory computer-readable storage medium of claim 8, wherein K is the Lipschitz constant.

13. The non-transitory computer-readable storage medium of claim 8, wherein minimizing the loss function on the linear model over possible values of the set of parameters θ

to produce an optimal set of parameters θ

comprises;

minimizing according to

14. The non-transitory computer-readable storage medium of claim 8, wherein each column in the set of records represents one of a label feature and an observable feature describing health data of the patients.

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 health data for a plurality of patients, the database storing records comprising rows and columns, where the rows are associated with patients having a medical condition, and columns of the rows contain values describing health data for the patients, the operations comprising;

receiving a database query from a client device, the database query requesting a linear model describing correlations among values of columns in a set of records in the database, the database query specifying a degree of privacy to maintain for the restricted data;

performing the database query on the set of records in the database to produce a differentially private version of the linear model that maintains the specified degree of privacy for the restricted data, performing the query comprising;

modeling the values of the columns in the set of records in the database to produce a linear model comprising a set of parameters θ

that describe correlations among the values describing health data for the patients;

minimizing a loss function on the linear model over possible values of the set of parameters θ

to produce an optimal set of parameters θ

; and

perturbing the optimal set of parameters θ

to produce the differentially private version of the linear model, the set of parameters θ

perturbed by a factor defined by;

16. The system of claim 15, wherein the client device is associated with a privacy budget representing remaining queries available to the client device in terms of at least one of ε

and δ

, the operations further comprising;

determining whether decrementing the privacy budget by the specified degree of privacy exceeds the remaining queries available, wherein performing the database query is responsive to determining that decrementing the privacy budget by the specified degree of privacy does not exceed the remaining queries available; and

decrementing the privacy budget by the specified degree of privacy.

17. The system of claim 16, wherein the privacy budget is specified by an entity managing the database.

18. The system of claim 15, wherein the linear model is at least one of a linear regression classifier, a logistic regression classifier, and a support vector machine classifier.

19. The system of claim 15, wherein K is the Lipschitz constant.

20. The system of claim 15, wherein minimizing the loss function on the linear model over possible values of the set of parameters θ

to produce an optimal set of parameters θ

comprises;

minimizing according to