Data obfuscation system, method, and computer implementation of data obfuscation for secret databases

1. A method of obfuscating data in which output values of a data query are obfuscated in a repeatable manner, via the use of an Obfuscating Function (OF) while maintaining an amount of obfuscation within a range in which the obfuscated output values provide to a user information of a first level of granularity, the repeatable manner comprising generating a same value of obfuscated data for each data query having a same value of input data, wherein the OF uses a transformation based upon a method of weighted regression, including perturbing values to obtain a dependent variable for the weighted regression and based upon addition of random errors to the output values of the data query to form a dependent variable y for the weighted regression, and using a non-obfuscated data value as an independent variable.

2. The method of obfuscating data as claimed in claim 1 based upon minimizing a Total Weighted Error.

3. The method of obfuscating data as claimed in claim 2 in which the Total Weighted Error is defined to be a Sum of Squared Errors.

4. The method of obfuscating data of claim 1 , in which frequency data having a first level of granularity is shown to the user at a second level of granularity, coarser than the first level of granularity, wherein the data is converted from the first level of granularity to the second level of granularity according to a rule other than a simple proximity of the data to a nearest value at the second level of granularity.

5. The method of obfuscating data, as claimed in claim 4 , that is a mapping based method based on a last digit of the data.

6. The method of obfuscating data, as claimed in claim 4 , using random rounding based upon an odd number being rounded up and an even number being rounded down.

7. The method of obfuscating data, as claimed in claim 4 , using random rounding based upon an even number being rounded up and an odd number being rounded down.

8. The method of obfuscating data, as claimed in claim 4 , using a pseudo random number seeding function which seeds a random number generator with input values, the output of the random number generator either rounded up or rounded down, or results shifted by a rounded amount up or down.

9. The method of obfuscating data, as claimed in claim 4 , using a pseudo random number seeding function which seeds a random number generator with input values, the output of the random number generator either shifted up or down a number of granularity factors.

10. The method of obfuscating data as claimed in claim 1 in which a high frequency filter is configured to hamper attempts to determine non-obfuscated values from obfuscated values.

11. The method of obfuscating data as claimed in claim 1 in which the obfuscated values are anticipated and pre-calculated.

12. The method of obfuscating data as claimed in claim 1 wherein the data request consists of multiple logical components, each being applied separately.

13. The method of obfuscating data as claimed in claim 12 in which restrictions are applied to the data relating to the granularity of the frequency data.

14. The method of obfuscating data as claimed in claim 1 in which the amount of obfuscation is altered in response to an algorithm.

15. The method of obfuscating data as claimed in claim 14 where the alteration of the amount of obfuscation is made in response to different use access rights.

16. The method of obfuscating data as claimed in claim 15 where the alteration of the amount of obfuscation is made in response to different levels of frequency of output aggregation allowing the data to be of a quality to make business decisions.

17. The method of obfuscating data as claimed in claim 1 wherein the frequency of each output value is determined at a first level of granularity and converted to a second level of granularity, coarser than the first level of granularity, wherein the data is converted from the first level of granularity to the second level of granularity according to a rule using a pseudo random number seeding function.

18. A method of representing data having a first level of granularity and a second level of granularity, coarser than the first level of granularity, wherein the data is converted from the first level of granularity to the second level of granularity according to a rule using a pseudo random number seeding function which seeds a random number generator with input values, the output of the random number generator either rounded up or rounded down, or results shifted by a rounded amount up or down.

19. The method of representing data having a first level of granularity and a second level of granularity, coarser than the first level of granularity, as claimed in claim 18 , wherein the output of the random number generator is either shifted up or down a number of granularity factors.

20. The method of representing data having a first level of granularity and a second level of granularity, coarser than the first level of granularity as claimed in claim 18 in which the values of the data at the second level of granularity are anticipated and pre-calculated.

21. The method of representing data having a first level of granularity and a second level of granularity, coarser than the first level of granularity as claimed in claim 18 in which the coarseness of the second level of granularity is altered in response to an algorithm.

22. The method of representing data having a first level of granularity and a second level of granularity, coarser than the first level of granularity as claimed in claim 21 where the coarseness of the second level of granularity is made in response to different use access rights.

23. The method of representing data having a first level of granularity and a second level of granularity, coarser than the first level of granularity as claimed in claim 22 where the coarseness of the second level of granularity is made in response to different levels of frequency of output aggregation allowing the data to be of a quality to make business decisions.

24. A computer implemented obfuscation system comprising:

at least one processor; and

memory storing data instructions that when executed by the at least one processor cause the at least one processor to:

run a query on data in a secret database to produce output data; and

obfuscate the output data using a repeatable obfuscation function to return obfuscated data in response to the query, wherein the repeatable obfuscation function uses a transformation used in mathematical modeling, based upon the method of weighted regression including perturbing values to obtain a dependent variable for the weighted regression and based upon addition of random errors to true values to form a dependent variable y for weighted regression, and using a non-obfuscated data value as an independent variable.

25. The system as claimed in claim 24 wherein the obfuscated values are anticipated and pre-calculated.

26. The system as claimed in claim 24 in which the amount of obfuscation is altered in response to an algorithm.

27. The system as claimed in claim 26 where the alteration of the amount of obfuscation is made in response to different use access rights.

28. The system as claimed in claim 27 where the alteration of the amount of obfuscation is made in response to different levels of frequency of output aggregation allowing the data to be of a quality to make business decisions.