Public preview
This feature is in public preview. It is available to all customers and can be enabled on the Immuta app settings page.
Project owners can use policy adjustments to increase a data set's utility while retaining the amount of k-anonymization that upholds de-identification requirements. With this feature enabled, users can redistribute the noise across multiple columns of a data source within a project to make specific columns more useful for their analysis. Since these adjustments only occur within the project and do not change the individual data policies, data users must be acting under the project to see the adjustments in the data source.
For example, a policy might mask these data source columns with k-anonymization: Income, Education, EmploymentStatus, Gender, and Location Code. When the analyst examines the data, the percent NULL has been predetermined by Immuta with an equal weight across all of these columns. However, if the analyst's work hinges on the EmploymentStatus column, the project owner can adjust the weights on the policy adjustment tab in the project to make the necessary data (EmploymentStatus) less NULL.
For columns that are already well-disclosed (meaning they already have a low percent null), the same percent null will display even when you drastically change the weight distribution.
Increasing the weight of a column that is already well-disclosed will not change the outcome. Generally, the biggest impact will be seen when you increase the weights of the largest percent null column. (The only exception to this is if that column already has a lot of native nulls in the remote database.)
This feature provides an option to allow fields in the clear when creating a purpose, permitting specified analysts to bypass k-anonymization in specific circumstances.
When any purpose with the allow fields in the clear property enabled is approved for use within a project, a project member can proceed through the policy adjustment workflow and specify columns to be unmasked.