Data Masking vs RedactionData masking and redaction are associated with different goals and methods. Data masking is primarily associated with creating test data and training data by removing personal or confidential information from production data. The goal of data masking it to maintain the same structure of data so that it will work in applications. This often requires shuffling and replacement algorithms that leave data types such as numbers and dates intact.Redaction is used to release human readable information without disclosing something personally identifiable or classified. For example, a study may release its detailed notes but redact the names of study participants. As such, redaction completely removes or blacks out sensitive fields or content.
|Data Masking vs Redaction
Removing sensitive data while maintaining the same structure so that data remains functional for processes such as testing and training.
Removing sensitive information in a secure manner such as removal, encryption or blacking out.