Which method creates a structurally similar, inauthentic version of data for testing purposes?

Data masking creates a structurally similar, inauthentic version of data that can be used for testing purposes. This method transforms sensitive information into a format that maintains the original data's structure but alters the actual values to prevent exposure of sensitive information.

In practice, data masking allows developers and testers to work with realistic data representations without risking the privacy or confidentiality of the original data. It is particularly useful in development and testing environments where real data cannot be used due to security and compliance concerns.

While data scrubbing focuses on cleansing data to ensure accuracy and consistency, and data anonymization aims to irreversibly alter data to prevent identification of individuals, data masking specifically retains the structural characteristics of the original data while making it unusable for identification. Data archiving, on the other hand, is related to the long-term storage of data rather than creating usable test versions. Thus, data masking stands out as the most relevant method for the context of creating inauthentic data for testing.