Even small extracts of data need to be created with caution if they are for public consumption. Sensitive data can 'hide' in unexpected places, and apparently innocuous data can be combined with other information to expose information about identifiable individuals. If we need to deliver an entire database in obfuscated form, the problems can get harder. Phil Factor examines some of the basic data masking techniques and the challenges inherent in masking certain types of sensitive and personal data while ensuring it still looks like the real data and preserving its referential integrity and distribution characteristics.
This article describes the practicalities of data masking, the various methods we can use, and the potential pitfalls. In subsequent articles, I'll demonstrate how we can mask or sanitize different types of data using tools such as SQL Clone, Data Masker for SQL Server, and SQL Data Generator.
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