Developer Transformation Guide

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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 datatypes.
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 datatypes.
Substitution: Replaces a column of data with similar but unrelated data from a dictionary. You can mask the string datatype.
Dependent: Replaces the values of one source column based on the values of another source column. You can mask the string datatype.
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 datatype.
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.