What technique enhances privacy by concealing parts of sensitive data during processing?

Data masking is a technique that enhances privacy by modifying sensitive data to protect it from unauthorized access while still allowing it to be used in a meaningful way for processing or analysis. This process involves replacing sensitive data elements with non-sensitive equivalents, ensuring that the original data cannot be reconstructed from the masked values.

For instance, in a database containing personally identifiable information (PII), data masking would alter names and Social Security numbers to generic placeholders so that those working with the data cannot identify individuals. This allows organizations to use datasets for application development, testing, or analytics without exposing sensitive information.

Other techniques, like data encryption, focus on transforming data so that it cannot be read without the appropriate decryption keys, which does not allow for processing in a usable format. Data obfuscation serves a similar purpose but often entails making data indistinct or unclear rather than replacing it. Data redaction involves editing a document to remove sensitive information, making it unsuitable for processing as it effectively removes data instead of altering it. Thus, data masking stands out because it balances data usability and privacy, allowing processes to occur without revealing sensitive information.