This white paper introduces data masking as a core privacy-enhancing technology to protect sensitive information such as PII and PHI while preserving usability for analytics. It distinguishes between static data masking, which scrubs data copies for testing and training, and dynamic masking, which applies rules at query time to protect live data, ensuring compliance and utility. Techniques include null replacement, hashing, regex masking, format-preserving masking, and k-anonymization. Best practices stress sensitive data discovery, balancing privacy with analytical needs, preserving referential integrity, and ensuring scalability for future growth.

Join for free to read