Data masking obscures sensitive data fields or replaces them with scrubbed values. Only being able to see the last four digits of a stored credit card when you’re checking out on an ecommerce site is an example of data masking
Why would someone use data masking?
Masking is used to allow access to full data records while preventing access to certain sensitive attribute values. For software and analytic development objectives, actual samples from real data can be gathered and processed with appropriate data scrubbing and masking to create realistic test frameworks while still supporting data protection principles.
When would data collaborators choose to use data masking?
While pseudonymization techniques are used by LiveRamp to protect consumer privacy throughout LiveRamp, some non-directly identifiable personal data may still be too sensitive to share with a data collaborator. LiveRamp therefore allows a data owner to specify additional fields for masking, thereby maintaining record-level analytics and record-level modeling with a data collaborator without fear of exposing the sensitive non-directly identifiable personal data attribute values.
Learn more about the power of data collaboration.