Method and system for making multisite performance measure anonymous and for controlling actions and re-identification of anonymous data

• US 10,025,840 B2
• Filed: 12/09/2013
• Issued: 07/17/2018
• Est. Priority Date: 12/10/2012
• Status: Active Grant

• Current Assignee: Koninklijke Philips N.V.
• Original Assignee: Koninklijke Philips N.V.
• Inventors: Gross, Brian David, Eldo, Issac
• Primary Examiner(s): Morrison, Jay A
• Assistant Examiner(s): Hoang, Ken
• Application Number:
  US14/647,605
• Publication Number:
  US 20150302070A1
• Time in Patent Office: 1,681 Days
• Field of Search: 707E17014, 707E17005, 707E17044, 707769, 707737, 707748, 707E17002, 707E17008, 707E17046, 707602, 707692, 707723, 707736, 707803, 707805, 707809, 707E17001, 707E17007, 707E17009, 707E17011, 707E17032, 707E17033, 707E17045, 707E17082, 707E1709, 707E17108, 707E17127, 707600, 707601, 707607, 707608, 707609, 707622, 707687, 707690, 707693, 707695, 707696, 707706, 707708, 707711, 707732, 707733, 707739, 707740, 707741, 707743, 707752, 707754, 707755, 707756, 707758, 707765, 707771, 707774, 707784, 707785, 707792, 707797, 707798, 707802, 707804, 707806, 707808, 707812, 705 439, 705 1453, 705 1423, 705 1435, 705 264, 705 2663, 705 271, 705 3, 705306, 705342, 705 44, 705 711, 705 713, 705 717, 705 727, 705 728, 705 731, 705 741, 709 1, 709226, 709224, 709201, 709204, 709203, 709222, 709227, 715764, 715763, 715771, 715230, 715246, 715255, 715760, 715781, 715810, 715856, 715863
• CPC Class Codes: G06F 16/20 of structured data, e.g. relational data
• CPC Class Codes: G06F 16/258 Data format conversion from or to a database
• CPC Class Codes: G06F 16/955 using information identifiers, e.g. uniform resource locators [URL]
• CPC Class Codes: H04L 67/10 in which an application is distributed across nodes in the network software deployment G06F8/60; multiprogramming arrangements G06F9/46

Abstract

A system for making data source anonymous including a plurality of data sources, each data source including a data creation engine which creates normalized data in accordance with a common schema and a transformation engine which de-normalizes the normalized data such that the data source cannot be inferred by other data sources and transmits the de-normalized data to a remote computing resource. A remote computing resource receives and stores the de-normalized data from the plurality of data sources.

Claims

1. A data source for making data source anonymous, the data source comprising:

a data creation engine which creates normalized data in accordance with a common schema, wherein the normalized data comprises a unique attribute from which the identity of the data source is capable of being inferred; and

a transformation engine which de-normalizes the normalized data such that the data source cannot be inferred by other data sources and transmits the de-normalized data to a remote computing resource, wherein de-normalizing the normalized data comprises;

determining a target quantity for the unique attribute to populate each of a plurality of data groupings,

splitting the normalized data into a plurality of groupings so that the unique attribute for each grouping does not exceed the target quantity, and

associating each grouping of the plurality of groupings with a respective unique identifier,

wherein the de-normalized data includes the plurality of groupings including respective unique identifiers.

2. The data source according to claim 1, wherein the transformation engine generates a table of global unique identifiers to map each unique attribute in the de-normalized data back to the normalized data format.

3. The data source according to claim 1, wherein the data source is a healthcare provider and the normalized data is clinical data.

4. The data source of claim 1, wherein:

de-normalizing the normalized data further comprises determining an additional target quantity for an additional unique attribute of the normalized data; and

splitting the normalized data comprises splitting the normalized data into a plurality of groupings so that the unique attribute for each grouping does not exceed the target quantity and the additional unique attribute does not exceed the additional target quantity.

5. The data source of claim 1, wherein determining the target quantity comprises using a lowest common denominator of the unique attribute across the normalized data entries as the target quantity.

6. A method for making data source anonymous, the method comprising:

obtaining normalized data in accordance with a common schema by a plurality of data sources, wherein the normalized data comprises a unique attribute from which the identity of the data source is capable of being inferred;

de-normalizing the normalized data such that the data source cannot be inferred by other data sources by a transformation engine, comprising;

determining a target quantity for the unique attribute to populate each of a plurality of data groupings,

splitting the normalized data into a plurality of groupings so that the unique attribute for each grouping does not exceed the target quantity, and

associating each grouping of the plurality of groupings with a respective unique identifier,

wherein the de-normalized data includes the plurality of groupings including respective unique identifiers; and

transmitting the de-normalized data to a remote computing resource.

7. The method of claim 6, further comprising generating a table of global unique identifiers to map each unique attribute in the de-normalized data back to the normalized data format.

8. The method of claim 6, wherein the normalized data is clinical data.

9. The method of claim 6, wherein obtaining the normalized data comprises creating the normalized data.

10. The method of claim 6, wherein:

de-normalizing the normalized data further comprises determining an additional target quantity for an additional unique attribute of the normalized data; and

splitting the normalized data comprises splitting the normalized data into a plurality of groupings so that the unique attribute for each grouping does not exceed the target quantity and the additional unique attribute does not exceed the additional target quantity.

11. The method of claim 6, wherein determining the target quantity comprises using a lowest common denominator of the unique attribute across the normalized data entries as the target quantity.

12. A transformation engine device for making data source anonymous, the transformation engine device comprising:

a memory and a processor configured to;

receive normalized data in accordance with a common schema from a plurality of data sources, wherein the normalized data comprises a unique attribute from which the identity of the data source is capable of being inferred, and

de-normalize the normalized data such that the data source cannot be inferred by other data sources and stored the de-normalized data, wherein the de-normalizing comprises;

determining a target quantity for the unique attribute to populate each of a plurality of data groupings,

splitting the normalized data into a plurality of groupings so that the unique attribute for each grouping does not exceed the target quantity, and

associating each grouping of the plurality of groupings with a respective unique identifier,

wherein the de-normalized data includes the plurality of groupings including respective unique identifiers.

13. The system according to claim 12, wherein processor further controls access to and re-identification of the de-normalized data.

14. The system according to claim 13, wherein the processor further controls the actions to be invoked on the de-normalized data based on the role of the user logged into the data provider.

15. The system according to claim 14, wherein the processor further receives a certificate from respective ones of the data sources to invoke a re-identification action.

16. The system according to claim 15, wherein the processor further compares the certificate transmitted from the data source to a stored certificate to authenticate the re-identification action.

17. The system according to claim 13, wherein the processor further generates a table of global unique identifiers to map each unique attribute in the de-normalized data back to the normalized data format to re-identify the normalized data.

18. The transformation engine of claim 12, wherein:

de-normalizing the normalized data further comprises determining an additional target quantity for an additional unique attribute of the normalized data; and

splitting the normalized data comprises splitting the normalized data into a plurality of groupings so that the unique attribute for each grouping does not exceed the target quantity and the additional unique attribute does not exceed the additional target quantity.

19. The transformation engine of claim 12, wherein determining the target quantity comprises using a lowest common denominator of the unique attribute across the normalized data entries as the target quantity.