Artificial data generation for differential privacy

Patent metadata

Field	Value
Publication number	US-2025131116-A1
Application number	US-202318490914-A
Country	US
Kind code	A1
Filing date	Oct 20, 2023
Priority date	Oct 20, 2023
Publication date	Apr 24, 2025
Grant date	—

Abstract

An embodiment configures a plurality of parameters, the parameters being usable to generate artificial data from original data, the configuring adjusting a level of privacy in the artificial data. An embodiment fits a distribution type to a variable of the original data. An embodiment adjusts, using a desired level of privacy and the distribution type, a level of noise, wherein the level of noise corresponds to the desired level of privacy. An embodiment generates, using the distribution type and the level of noise, the artificial data, the artificial data achieving the desired level of privacy by including noise data corresponding to the level of noise.

First claim

What is claimed is:

1 . A computer-implemented method comprising:
configuring a plurality of parameters, the parameters being usable to generate artificial data from original data, the configuring adjusting a level of privacy in the artificial data; fitting a distribution type to a variable of the original data; adjusting, using a desired level of privacy and the distribution type, a level of noise, wherein the level of noise corresponds to the desired level of privacy; and generating, using the distribution type and the level of noise, the artificial data, the artificial data achieving the desired level of privacy by including noise data corresponding to the level of noise.

2 . The computer-implemented method of claim 1 , wherein configuring the plurality of parameters comprises setting an upper bound parameter of a continuous variable comprising the original data to a first value according to a statistical characteristic of the continuous variable.

3 . The computer-implemented method of claim 1 , wherein configuring the plurality of parameters comprises setting a lower bound parameter of a continuous variable comprising the original data to a second value according to a statistical characteristic of the continuous variable.

4 . The computer-implemented method of claim 1 , wherein the variable contributes to a privacy aspect of the original data.

5 . The computer-implemented method of claim 1 , wherein fitting a distribution type to the variable of the original data further comprises:
selecting, from a plurality of distribution type fittings according to a goodness of fit statistic computed on each distribution type fitting, the distribution type.

6 . The computer-implemented method of claim 1 , wherein the desired level of privacy is higher than a level of privacy in the original data.

7 . A computer program product comprising one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions executable by a processor to cause the processor to perform operations comprising:
configuring a plurality of parameters, the parameters being usable to generate artificial data from original data, the configuring adjusting a level of privacy in the artificial data; fitting a distribution type to a variable of the original data; adjusting, using a desired level of privacy and the distribution type, a level of noise, wherein the level of noise corresponds to the desired level of privacy; and generating, using the distribution type and the level of noise, the artificial data, the artificial data achieving the desired level of privacy by including noise data corresponding to the level of noise.

8 . The computer program product of claim 7 , wherein the stored program instructions are stored in a computer readable storage device in a data processing system, and wherein the stored program instructions are transferred over a network from a remote data processing system.

9 . The computer program product of claim 7 , wherein the stored program instructions are stored in a computer readable storage device in a server data processing system, and wherein the stored program instructions are downloaded in response to a request over a network to a remote data processing system for use in a computer readable storage device associated with the remote data processing system, further comprising:
program instructions to meter use of the program instructions associated with the request; and program instructions to generate an invoice based on the metered use.

10 . The computer program product of claim 7 , wherein configuring the plurality of parameters comprises setting an upper bound parameter of a continuous variable comprising the original data to a first value according to a statistical characteristic of the continuous variable.

11 . The computer program product of claim 7 , wherein configuring the plurality of parameters comprises setting a lower bound parameter of a continuous variable comprising the original data to a second value according to a statistical characteristic of the continuous variable.

12 . The computer program product of claim 7 , wherein the variable contributes to a privacy aspect of the original data.

13 . The computer program product of claim 7 , wherein fitting a distribution type to the variable of the original data further comprises:
selecting, from a plurality of distribution type fittings according to a goodness of fit statistic computed on each distribution type fitting, the distribution type.

14 . The computer program product of claim 7 , wherein the desired level of privacy is higher than a level of privacy in the original data.

15 . A computer system comprising a processor and one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions executable by the processor to cause the processor to perform operations comprising:
configuring a plurality of parameters, the parameters being usable to generate artificial data from original data, the configuring adjusting a level of privacy in the artificial data; fitting a distribution type to a variable of the original data; adjusting, using a desired level of privacy and the distribution type, a level of noise, wherein the level of noise corresponds to the desired level of privacy; and generating, using the distribution type and the level of noise, the artificial data, the artificial data achieving the desired level of privacy by including noise data corresponding to the level of noise.

16 . The computer system of claim 15 , wherein configuring the plurality of parameters comprises setting an upper bound parameter of a continuous variable comprising the original data to a first value according to a statistical characteristic of the continuous variable.

17 . The computer system of claim 15 , wherein configuring the plurality of parameters comprises setting a lower bound parameter of a continuous variable comprising the original data to a second value according to a statistical characteristic of the continuous variable.

18 . The computer system of claim 15 , wherein the variable contributes to a privacy aspect of the original data.

19 . The computer system of claim 15 , wherein fitting a distribution type to the variable of the original data further comprises:
selecting, from a plurality of distribution type fittings according to a goodness of fit statistic computed on each distribution type fitting, the distribution type.

20 . The computer system of claim 15 , wherein the desired level of privacy is higher than a level of privacy in the original data.

21 . A data processing system comprising a processor and one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions executable by the processor to cause the processor to perform operations comprising.

22 . The data processing system of claim 21 , wherein configuring the plurality of parameters comprises setting an upper bound parameter of a continuous variable comprising the original data to a first value according to a statistical characteristic of the continuous variable.

23 . The data processing system of claim 21 , wherein configuring the plurality of parameters comprises setting a lower bound para

Assignees

• IBM

Inventors

• Han Si Er
• Xu Jing
• Ma Xiao Ming
• Xu Jing James
• Kang jiang bo
• Zhang xue ying
• Wang Jun
• Yang ji hui

Classifications

• G06N3/044

  Recurrent networks, e.g. Hopfield networks · CPC title

• G06N3/08

  Learning methods · CPC title

• G06N7/01

  Probabilistic graphical models, e.g. probabilistic networks · CPC title

• G06N3/047

  Probabilistic or stochastic networks · CPC title

• G06N20/00

  Machine learning · CPC title