USING USER-ATTRIBUTE ONTOLOGIES TO CALCULATE USER-ANONYMITY DEGREES

• US 20110173261A1
• Filed: 01/07/2011
• Published: 07/14/2011
• Est. Priority Date: 01/08/2010
• Status: Active Grant

• Current Assignee: Cerner Innovation, Inc. (Oracle Corporation)
• Original Assignee: Cerner Innovation, Inc. (Oracle Corporation)
• Inventors: MCCALLIE, DAVID P. Jr., KOLM, MARGARET CUSHING, FINN, CHRISTOPHER S.
• Granted Patent: US 8,918,462 B2
• Time in Patent Office: Days
• US Class Current: 709/204
• CPC Class Codes: G06F 21/6245 Protecting personal data, e.g. for financial or medical purposes
• CPC Class Codes: G16H 10/60 for patient-specific data, e.g. for electronic patient records
• CPC Class Codes: G16H 40/67 for remote operation
• CPC Class Codes: H04L 63/0407 wherein the identity of one or more communicating identities is hidden cryptographic mechanisms or cryptographic arrangements for anonymous credentials or for identity based cryptographic systems H04L9/00
• CPC Class Codes: H04L 67/12 specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks

Abstract

Computerized methods and systems for using user-attribute ontologies to calculate anonymity levels are provided. A semi-automated mechanism permits selective and optionally progressive exposure of demographic and clinical information to other members of a medical social network group that a member has joined. When a user decides to join a medical social network group (which may be based upon system suggestion or initiated of the user'"'"'s own accord), he or she selects or creates a profile that has a statistically validated degree of anonymity, ranging from fully anonymous to fully transparent. Based upon a user'"'"'s elected level of exposure, only certain data is exposed to the rest of the group'"'"'s membership. Thus, the user is permitted to personally balance the tension between exposing a level of information that may render him or her personally identifiable and exposing enough information to obtain meaningful advice and/or camaraderie.

Claims

1. One or more computer-readable storage media having computer-executable instructions embodied thereon for performing a method in a clinical computing environment for using user-attribute ontologies to calculate user-anonymity degrees, the method comprising:

receiving an indication that a user desires to join a medical social network group having a plurality of members;

receiving a plurality of attribute values, each attribute value being associated with a user-attribute;

calculating a degree of user-anonymity conditioned on exposure of at least one subset of the plurality of attribute values; and

presenting an indication of the calculated degree of user-anonymity and an identity of each of the plurality of attribute values included in the at least one subset.

2. The one or more computer-readable storage media of claim 1,

wherein a first and a second of the plurality of attribute values are associated with a common user-attribute,

wherein the common user-attribute includes a multi-level user-attribute ontology related thereto,

wherein each of the first and second of the plurality of attribute values is associated with a different level of the multi-level user attribute ontology,

and wherein the at least one subset of the plurality of attribute values includes at least one of the first and second of the plurality of attribute values.

3. The one or more computer-readable storage media of claim 1, wherein the method further comprises receiving group-member attribute values for a plurality of members of the medical social network group, and wherein calculating the degree of user-anonymity comprises calculating the degree of user-anonymity as it relates to the received group-member attribute values.

4. The one or more computer-readable storage media of claim 3, wherein at least a portion of the received group-member attribute values is derived or extracted from information an associated group member has elected to expose to other members of the medical social network group.

5. The one or more computer-readable storage media of claim 3, wherein at least a portion of the group-member attribute values is derived or extracted from a health record of an associated group member.

6. The one or more computer-readable storage media of claim 5, wherein at least a portion of the received group-member attribute values is derived or extracted from information the associated group member has elected to expose to other members of the medical social network group, and wherein the information comprises a subset of the group-member attribute values derived or extracted from the health record of the associated group member.

7. The one or more computer-readable storage media of claim 1,

wherein calculating the degree of user-anonymity conditioned on exposure of at least one subset of the plurality of attribute values comprises calculating a plurality of varying degrees of user-anonymity, each of the plurality of varying degrees of user-anonymity being conditioned, at least in part, on exposure of a different subset of the plurality of attribute values,

and wherein presenting the indication of the calculated degree of user-anonymity and the identity of each of the plurality of attribute values included in the at least one subset comprises presenting an indication of each of the plurality of varying degrees of user-anonymity and the identity of each of the plurality of attribute values included in each different subset of the plurality of attribute values.

8. The one or more computer-readable storage media of claim 1, wherein at least a portion of the plurality of attribute values is derived or extracted from information the user has elected to expose to other members of the medical social network group.

9. The one or more computer-readable storage media of claim 1, wherein at least a portion of the plurality of attribute values is derived or extracted from a health record of the user.

10. The one or more computer-readable storage media of claim 9, wherein at least a portion of the plurality of attribute values is derived or extracted from information the user has elected to expose to other members of the medical social network group, and wherein the information comprises a subset of the plurality of attribute values derived or extracted from the health record of the user.

11. A method in a clinical computing environment for providing medical social network group suggestions having anonymity degrees calculated, at least in part, using user-attribute ontologies, the method comprising:

extracting a plurality of attribute values from a health record of a user, each attribute value being associated with a user-attribute;

receiving group-member attribute values for a plurality of members of a medical social network group, the group-member attribute values being associated with a common user-attribute associated with the plurality of attribute values;

calculating a degree of user-anonymity as it relates to the received group-member attribute values, the degree of user-anonymity being conditioned on exposure of at least one subset of the plurality of attribute values; and

presenting a suggestion that the user join the medical social network group and the calculated degree of user-anonymity upon determining that the calculated degree of user-anonymity meets or exceeds a threshold degree of user-anonymity.

12. The method of claim 11,

wherein a first and a second of the plurality of attribute values are associated with a common user-attribute,

wherein the common user-attribute includes a multi-level user-attribute ontology related thereto,

wherein each of the first and second of the plurality of attribute values is associated with a different level of the multi-level user-attribute ontology,

and wherein the at least one subset of the plurality of attribute values includes at least one of the first and second of the plurality of attribute values.

13. The method of claim 12,

wherein calculating the degree of user-anonymity comprises calculating a plurality of degrees of user-anonymity each conditioned on exposure of a different subset of the plurality of attribute values, at least a first of the different subsets containing the first of the plurality of attribute values and a second of the different subsets containing the second of the plurality of attribute values,

and wherein presenting a suggestion that the user join the medical social network group and the calculated degree of user-anonymity upon determining that the calculated degree of user-anonymity meets or exceeds the threshold degree of user-anonymity comprises presenting the suggestion that the user join the medical social network group and each of the plurality of calculated degrees of user-anonymity upon determining that at least one of the plurality of calculated degrees of user-anonymity meets or exceeds the threshold degree of user-anonymity.

14. The method of claim 11, wherein calculating the degree of user-anonymity comprises calculating the degree of user-anonymity using a statistical blurring methodology.

15. The method of claim 11, wherein at least a portion of the received group-member attribute values is derived or extracted from a health record of an associated group member.

16. The method of claim 15, wherein at least a portion of the received group-member attribute values is derived or extracted from a set of information the associated group member has elected to reveal to other members of the medical social network group, and wherein the set of information comprises a subset of the group-member attribute values derived or extracted from the health record of the associated group member.

17. The method of claim 11, wherein at least a portion of the received group-member attribute values is derived or extracted from a set of information an associated group member has elected to reveal to other members of the medical social network group.

18. One or more computer-readable storage media having computer-executable instructions embodied thereon for performing a method in a clinical computing environment for using user-attribute ontologies to calculate user-anonymity degrees, the method comprising:

receiving an indication that a user desires to join a medical social network group having a plurality of members;

receiving a plurality of attribute values, each attribute value being associated with a user-attribute, wherein a first and a second of the plurality of attribute values are associated with a first common user-attribute, wherein the first common user-attribute includes a first multi-level user-attribute ontology related thereto, and wherein each of the first and second of the plurality of attribute values is associated with a different level of the first multi-level user-attribute ontology, wherein a third and a fourth of the plurality of attribute values are associated with a second common user-attribute, wherein the second common user-attribute includes a second multi-level user-attribute ontology related thereto, and wherein each of the third and fourth of the plurality of attribute values is associated with a different level of the second multi-level user-attribute ontology,

calculating a plurality of degrees of user-anonymity conditioned on exposure of varying subsets of the plurality of attribute values, a first subset containing at least one of the first and second of the plurality of attribute values and one of the third and fourth of the plurality of attribute values; and

presenting an indication of the calculated degrees of user-anonymity and an identity of each of the plurality of attribute values included in the associated subset.

19. The one or more computer-readable storage media of claim 18, wherein at least a portion of the plurality of attribute values is derived or extracted from a health record of the user.

20. The one or more computer-readable storage media of claim 18, wherein the method further comprises receiving group-member attribute values for a plurality of members of the medical social network group, and wherein calculating the plurality of degrees of user-anonymity comprises calculating each of the plurality of degrees of user-anonymity as it relates to the received group-member attribute values.