Policy Adjustments and HIPAA Expert Determination in Immuta (BETA)
Audience: Project Owners
Content Summary: This page outlines the Policy Adjustment and Expert Determination features.
These features must be enabled through advanced configuration and are only available to select licenses. Please contact your Immuta representative for help with enabling this feature.
Policy Adjustments and HIPAA Expert Determination are tools project owners can use to increase a data set's usability while retaining the required amount of k-anonymization to uphold de-identification requirements. With these features enabled, users can redistribute the noise across multiple columns of a data source within a project to gain utility. Since these adjustments only occur within the project and do not update the individual Data Policies, data users must be acting under the project to see the adjustments in the data source.
Policy Adjustments are available on all equalized projects with a noise reduction purpose applied, while Expert Determination is only available on projects with the Re-identification Prohibited.HIPAA De-identification or Re-identification Prohibited.Expert Determination purposes, since it is specific to the HIPAA De-identification Global Policy. Once a policy has been adjusted, Expert Determination provides a downloadable report that contains an algorithmic statistical analysis of the data source to assess the very small re-identification probability indicated by the purpose.
For an example, let's look at this data source with the columns Account Type, Education, EmploymentStatus, Gender, and Location Code masked using k-anonymization. When the analyst looks at the data, the percent NULL has been predetermined by Immuta with an equal weight across all these columns. However, this analyst's work is hinging on the EmploymentStatus column. The project owner can come in to policy adjustments and adjust the weights to make the necessary data (EmploymentStatus) less NULL.
Here the default weight has been equalized across the columns giving the same amount of importance to all of the data, allocating the noise to allow the most use possible across all of the masked columns.
Here the weight is manually adjusted to lower the percent NULL and make the needed column (EmploymentStatus) more usable while still retaining the necessary amount of de-identification by redistibuting the noise accross the other columns.