Differentially private processing and database storage

• US 10,586,068 B2
• Filed: 01/02/2019
• Issued: 03/10/2020
• 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
• Primary Examiner(s): Leung, Robert B
• Application Number:
  US16/238,437
• Publication Number:
  US 20190138743A1
• Time in Patent Office: 433 Days
• CPC Class Codes: G06F 16/2453 Query optimisation
• 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 16/248 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: G06N 20/00 Machine learning
• CPC Class Codes: G06N 20/10 using kernel methods, e.g. support vector machines [SVM]
• 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 linear model describing correlations among values of columns in a set of records in the database, 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 in the database to produce a differentially private version of the linear model that maintains the identified level of differential 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;

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

produced by optimizing a loss function perturbed by an additive term defined 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 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 and wherein R₂ is a radius value representing a radius of a hypersphere that includes values of the columns in the set of records.

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;

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 the restricted data.

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 linear model describing correlations among values of columns in a set of records in the database, 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 in the database to produce a differentially private version of the linear model that maintains the identified level of differential 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;

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

produced by optimizing a loss function perturbed by an additive term defined 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, 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 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 and wherein R₂ is a radius value representing a radius of a hypersphere that includes values of the columns in the set of records.

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;

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 the restricted data.

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 linear model describing correlations among values of columns in a set of records in the database, 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 (δ

)-differential privacy;

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 identified level of differential 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;

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

produced by optimizing a loss function perturbed by an additive term defined 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 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 and R₂ is a radius value representing a radius of a hypersphere that includes values of the columns in the set of records.

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;