Using user-attribute ontologies to calculate user-anonymity degrees

摘要

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.

主权项

1. One or more computer hardware 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 the plurality of attribute values comprises attribute values associated with user submitted data and attribute values extracted from the user's electronic health records, wherein a first user-attribute value of the plurality of attribute values is a medical condition, wherein the first user-attribute value includes a multi-level user-attribute ontology related thereto; receiving group-member attribute values for the plurality of members of the medical social network group; calculating a degree of user-anonymity for the user conditioned on exposure of at least one subset of the plurality of attribute values to the medical social network group, wherein the calculated degree of user-anonymity is based at least in part on the group-member 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.