PRIVACY MEASUREMENT AND QUANTIFICATION

• US 20150261959A1
• Filed: 02/20/2015
• Published: 09/17/2015
• Est. Priority Date: 03/14/2014
• Status: Active Grant

• Current Assignee: TATA Consultancy Services Limited (Tata Sons Pvt Ltd.)
• Original Assignee: TATA Consultancy Services Limited (Tata Sons Pvt Ltd.)
• Inventors: PAL, Arpan, UKIL, Arijit, BANDYOPADHYAY, Soma
• Granted Patent: US 9,547,768 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 11/3024 where the computing system component is a central processing unit [CPU]
• CPC Class Codes: G06F 11/3409 for performance assessment
• CPC Class Codes: G06F 21/577 Assessing vulnerabilities and evaluating computer system security
• CPC Class Codes: G06F 21/6254 by anonymising data, e.g. decorrelating personal data from the owner's identification
• CPC Class Codes: G06F 2221/034 Test or assess a computer or a system
• CPC Class Codes: Y04S 40/20 Information technology specific aspects, e.g. CAD, simulation, modelling, system security

Abstract

System(s) and method(s) to provide privacy measurement and privacy quantification of sensor data are disclosed. The sensor data is received from a sensor. The private content associated with the sensor data is used to calculate a privacy measuring factor by using entropy based information theoretic model. A compensation value with respect to distribution dissimilarity is determined. The compensation value compensates a statistical deviation in the privacy measuring factor. The compensation value and the privacy measuring factor are used to determine a privacy quantification factor. The privacy quantification factor is scaled with respect to a predefined finite scale to obtain at least one scaled privacy quantification factor to provide quantification of privacy of the sensor data.

Claims

1. A method to provide privacy measurement and privacy quantification of sensor data, the method comprising:

receiving sensor data from a sensor;

calculating a privacy measuring factor with respect to a private content and a non-private content, wherein the private content and the non-private content are associated with the sensor data, wherein the privacy measuring factor is calculated by using a computation technique, and wherein the privacy measuring factor depicts an amount of privacy with respect to the private content;

determining a compensation value with respect to a distribution dissimilarity of private content such that the compensation value compensates a statistical deviation in the privacy measuring factor, wherein the statistical deviation refers to a deviation in measurement of privacy while calculating the privacy measuring factor;

determining a privacy quantification factor by using the compensation value and the privacy measuring factor; and

scaling the privacy quantification factor with respect to a predefined finite scale to obtain at least one scaled privacy quantification factor, wherein the predefined scale comprises a finite set of values, and wherein each value from the finite set of values refers to quantification of privacy content associated with the sensor data;

wherein the receiving, the identifying, the calculating, the determining the compensation value, the determining the privacy quantification factor and the scaling are performed by a processor.

2. The method of claim 1, wherein the sensor data comprises time series data, and wherein the time series data comprises smart meter data, temperature data, or other sensor data.

3. The method of claim 1, wherein the computation technique comprises an entropy based information theoretical model.

4. The method of claim 1, wherein the statistical compensation value compensates an error in the privacy measuring factor, wherein the error is due to a statistical relation between private data and sensor data, wherein the private data and the sensor data are drawn from a similar distribution.

5. The method of claim 1, wherein the compensation value is determined based on an outcome of a two-sample Kolmogorov-Smirnov test.

6. The method of claim 5, wherein statistical compensation is computed using Wasserstein distance when two-sample Kolmogorov-Smirnov test finds a misfit between the distribution of private data and the sensor data.

7. The method of claim 1, wherein the privacy quantification factor is determined by using a product of the compensation value and the privacy measuring factor.

8. The method of claim 1, further comprising preserving the sensor data based on the scaled privacy quantification factor by using at least one privacy preservation technique, wherein the at least one privacy preservation technique comprising adding noise to the sensor data, wherein the noise is added equivalent to a privacy quantification value;

and wherein the noise to be added is directly proportional to the privacy quantification factor.

9. A system to provide privacy measurement and privacy quantification of sensor data, the system comprising:

a processor; and

a memory coupled to the processor, wherein the processor is capable of executing a plurality of modules stored in the memory, and wherein the plurality of modules comprising;

a receiving module configured to receive sensor data from a sensor;

a calculation module configured to calculate a privacy measuring factor with respect to a private content and a non-private content, wherein the private content and the non-private content are associated with the sensor data, wherein the privacy measuring factor is calculated by using a computation technique, and wherein the privacy measuring factor depicts an amount of privacy with respect to the private content;

a determination module configured to;

determine a compensation value with respect to a distribution dissimilarity of private content such that the compensation value compensates a statistical deviation in the privacy measuring factor, wherein the statistical deviation refers to a deviation in measurement of privacy while calculating the privacy measuring factor;

determine a privacy quantification factor by using the compensation value and the privacy measuring factor; and

a privacy quantification module configured to scale the privacy quantification factor with respect to a predefined finite scale to obtain at least one scaled privacy quantification factor, wherein the predefined finite scale comprises a finite set of values, and wherein each value from the finite set of values refers to quantification of privacy content associated with the sensor data.

10. The system of claim 9, further comprising preserving the sensor data based on the scaled privacy quantification factor by using at least one privacy preservation technique, wherein the at least one privacy preservation technique comprising adding noise to the sensor data.

11. The system of claim 9, wherein the sensor data comprises time series data, and wherein the time series data comprises smart meter data, temperature data, or other sensor data.

12. The system of claim 9, wherein the computation technique comprises an entropy based information theoretical model.

13. The system of claim 9, wherein the statistical compensation value compensates an error in the privacy measuring factor, wherein the error is due to a statistical relation between private data and sensor data, wherein the private data and the sensor data are drawn from a similar distribution.

14. The system of claim 9, wherein the compensation value is determined based on an outcome of a two-sample Kolmogorov-Smirnov test.

15. The system of claim 14, wherein statistical compensation is computed using Wasserstein distance when two-sample Kolmogorov-Smirnov test finds a misfit between the distribution of private data and the sensor data.

16. The system of claim 9, wherein the privacy quantification factor is determined by using a product of the compensation value and the privacy measuring factor.

17. A computer program product having embodied thereon a computer program to provide privacy measurement and privacy quantification of sensor data, the computer program product comprising:

a program code for receiving sensor data from at least one sensor;

a program code for calculating a privacy measuring factor with respect to a private content and a non-private content, wherein the private content and the non-private content are associated with the sensor data, wherein the privacy measuring factor is calculated by using an entropy computation, and wherein the privacy measuring factor depicts an amount of privacy with respect to the private content;

a program code for determining a compensation value with respect to a distribution dissimilarity of private content such that the compensation value compensates a statistical deviation in the privacy measuring factor, wherein the statistical deviation refers to a deviation in measurement of privacy while calculating the privacy measuring factor;

a program code for determining a privacy quantification factor by using the compensation value and the privacy measuring factor; and

a program code for scaling the privacy quantification factor with respect to a predefined finite scale to obtain at least one scaled privacy quantification factor, wherein the predefined scale comprises finite set of values, and wherein each value from the finite set of values refers to quantification of privacy content associated with the sensor data.

18. The computer program product of claim 17, further comprising a program code for preserving the sensor data based on the scaled privacy quantification factor by using at least one privacy preservation technique, wherein the at least one privacy preservation technique comprising adding noise to the sensor data.

19. The computer program product of claim 17, wherein the statistical compensation value compensates an error in the privacy measuring factor, wherein the error is due to a statistical relation between private data and sensor data, wherein the private data and the sensor data are drawn from a similar distribution.

20. The computer program product of claim 17, wherein the statistical compensation value is determined based on an outcome of a two-sample Kolmogorov-Smirnov test, wherein statistical compensation is computed using Wasserstein distance when two-sample Kolmogorov-Smirnov test finds a misfit between the distribution of private data and the sensor data.