Developer Transformation Guide

10.5
- 10.5.2
- 10.4.1
- 10.4.0

Back Next

Masking Techniques

The masking technique is the type of data masking to apply to the selected column.

You can select one of the following masking techniques for an input column:

Random: Produces random, non-repeatable results for the same source data and masking rules. You can mask date, numeric, and string datatypes. Random masking does not require a seed value. The results of random masking are non-deterministic.
Expression: Applies an expression to a source column to create or mask data. You can mask all data types.
Key: Replaces source data with repeatable values. The Data Masking transformation produces deterministic results for the same source data, masking rules, and seed value. You can mask date, numeric, and string data types.
Substitution: Replaces a column of data with similar but unrelated data from a dictionary. You can mask the string data type.
Dependent: Replaces the values of one source column based on the values of another source column. You can mask the string data type.
Tokenization: Replaces source data with data generated based on customized masking criteria. The Data Masking transformation applies rules specified in a customized algorithm. You can mask the string data type.
Encryption: Replaces source data with encrypted values based on encryption criteria that you configure in the transformation. You can encrypt the string data type.
Special Mask Formats: Credit card number, email address, IP address, phone number, SSN, SIN, or URL. The Data Masking transformation applies built-in rules to intelligently mask these common types of sensitive data.
No Masking: The Data Masking transformation does not change the source data.