Method And Apparatus For Privacy-Preserving Data Mapping Under A Privacy-Accuracy Trade-Off

• US 20150235051A1
• Filed: 08/19/2013
• Published: 08/20/2015
• Est. Priority Date: 08/20/2012
• Status: Abandoned Application

• Current Assignee: Thomson Licensing (Vantiva SA)
• Original Assignee: Thomson Licensing (Vantiva SA)
• Inventors: Fawaz, Nadia, Calmon, Flavio Du Pin
• Application Number:
  US14/420,476
• Publication Number:
  US 20150235051A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 21/6245 Protecting personal data, e.g. for financial or medical purposes

Abstract

A method for generating a privacy-preserving mapping commences by characterizing an input data set Y with respect to a set of hidden features S. Thereafter, the privacy threat is modeled to create a threat model, which is a minimization of an inference cost gain on the hidden features S. The minimization is then constrained by adding utility constraints to introduce a privacy/accuracy trade-off. The threat model is represented with a metric related to a self-information cost function. Lastly, the metric is optimized to obtain an optimal mapping, in order to provide a mapped output U, which is privacy-preserving.

Claims

1. A method of generating a privacy-preserving mapping of an input data set which is subject to a privacy threat, said method performed by a processor and comprising:

determining a relationship between said input data set Y and a set of hidden features S, wherein said relationship is not a deterministic function;

minimizing a metric on the hidden features S subject to utility constraints in order to obtain an optimal mapping, wherein said metric describes the privacy threat and is based on a self-information cost function and said utility constraints are based on a distortion between the input data set and an output of said privacy-preserving mapping; and

obtaining an output U of said optimal mapping, wherein said output is privacy-preserving on the hidden features.

2. The method of claim 1, wherein the step of minimizing comprises:

transforming said metric minimization into a convex optimization; and

solving said convex optimization.

3. The method of claim 1, wherein said metric is one of an average information leakage and a maximum information leakage of said set of hidden features S given said privacy-preserving mapping.

4. The method of claim 2, wherein the step of solving said convex optimization comprises:

using one of convex solver methods and interior-point methods.

5. The method of claim 1, wherein the step of determining comprises:

determining one of a joint probability density and a distribution function of the input data set Y and the hidden features S.

6. The method of claim 1, wherein the output U is a function of Y.

7. The method of claim 6, wherein the optimal mapping is of the type:

U=Y+Z, wherein Z is an additive noise variable and said utility constraint is a function of Z.

8. The method of claim 1, wherein the privacy-preserving mapping is used for privacy-preserving queries to a database, wherein S represents discrete entries to a database of n users, Y is a non-deterministic function of S, and U is a query output, such that the individual entries S are hidden to an adversary with access to U.

11. The method of claim 1, wherein the step of obtaining comprises:

sampling one of a probability density and a distribution function on U.

12. The method of claim 7, wherein the noise is one of Laplacian, Gaussian and pseudo-random noise.

13. The method of claim 1, wherein the step of minimizing is pre-processed.

14. The method of claim 1, wherein the step of determining is pre-processed.

9. (canceled)

10. (canceled)

15. An apparatus for generating a privacy-preserving mapping of an input data set which is subject to a privacy threat, said apparatus comprising:

a processor, for receiving at least one input/output; and

at least one memory in signal communication with said processor, said processor being configured to;

determine a relationship between said input data set Y and a set of hidden features S, wherein said relationship is not a deterministic function;

minimize a metric on the hidden features S subject to utility constraints in order to obtain an optimal mapping, wherein said metric describes the privacy threat and is based on a self-information cost function and said utility constraints are based on a distortion between the input data set and an output of said privacy-preserving mapping; and

obtain an output U of said optimal mapping, wherein said output is privacy-preserving on the hidden features.

16. The apparatus of claim 15, wherein said processor is configured to minimize by being configured to:

transform said metric minimization into a convex optimization; and

solve said convex optimization.

17. The apparatus of claim 15, wherein said metric is one of an average information leakage and a maximum information leakage of said set of hidden features S given said privacy-preserving mapping.

18. The apparatus of claim 15, wherein said processor is configured to solve said convex optimization by being configured to:

use one of convex solver methods and interior-point methods.

19. The apparatus of claim 15 wherein said processor is configured to determine a relationship by being configured to:

determine the joint probability density or distribution function of the input data set Y and the hidden features S.

20. The apparatus of claim 15, wherein the output U is a function of Y.

21. The apparatus of claim 20, wherein the optimal mapping performed by said processor is of the type:

U=Y+Z, wherein Z is an additive noise variable and said utility constraint (distortion) is a function of Z.

22. The apparatus of claim 15, wherein the privacy-preserving mapping performed by said processor is used for privacy-preserving queries to a database, wherein S represents discrete entries to a database of n users, Y is a non-deterministic function of S, and U is a query output, such that the individual entries S are hidden to an adversary with access to U.

25. The apparatus of claim 15, wherein said processor is configured to obtain a mapped output U by being configured to:

sample a probability density or distribution function on U.

26. The apparatus of claim 21, wherein the noise is one of Laplacian, Gaussian and pseudo-random noise.

27. The apparatus of claim 15, wherein the step of minimizing is pre-processed.

28. The apparatus of claim 27, wherein the step of determining is pre-processed.

23. (canceled)

24. (canceled)