Regular Expression Text Generator
This section provides a comprehensive description of the regular expression (regex) text generation transformation type.
For a summary of the rule and its compatibility with platform jobs and execution environments, see 
Masking Rule Types.
Data Types
The regex text generator supports the following data types:
Text
Description
The value is partially or completely replaced by a randomly generated string that matches the supplied regular expression. For example, for an initial value:
abcdef
you could use the following regular expression:
[a-z]{6}
This would produce an output such as:
mvskyc
Optionally specify Start Index and Length to Replace to replace a part of the input value with the generated text. For example, with an input of abcdef and a pattern of [0-9]{4}, replacing four characters from position 1, an output might be a4321f.
Masking Behavior
The options are described in the following table:
Option | Description |
|---|---|
Regular Expression | The pattern that the generated text should match. For more information about the regular expression syntax supported in the platform, see Regular Expression Syntax. (Click on the RegExp class in the Class Summary table.) |
Start Index | Where to start replacing the string. This is required for partial replacements. Start index 0 is the beginning of the string. Index 1 is after the first character of the string. |
Length to Replace | The length of the string to replace. This is required for partial replacements. The value entered will be the number of characters from the string that will be replaced. |
You can unmask fields that have been masked with the regex text generator.
Examples
Here are examples of regular expressions that you could use to match some example fields and formats:
Field | Format | Expression |
|---|---|---|
Email Address | xxxxxxx@xxxxx.com | [a-z]{7}\@[a-z]{5}\.com |
Last Name | xxxxxxxx | [a-z]{8} |
Tokenization Behavior
Tokenization Behavior contains various settings that determine how tokenization is performed when the rule is applied to a dataset.
Note
Some rules may not have all the settings described, as some of the settings are only appropriate for certain types of rules.
The Behavior setting determines whether a Token Vault is used to ensure consistency and to allow unmasking of the generated values. There are two options:
Preserve Data Consistency
Do not preserve data consistency, token duplication allowed.
Select Preserve Data Consistency to consistently tokenize values within the same Protected Data Domain (PDD). That is, re-use the same tokens for previously seen raw values to be able to preserve referential integrity for this field within the PDD (but not to other PDDs).
The other settings include:
Permit unmasking of original values
Retain NULL values
Tokenize without a vault (use NOVLT)
When consistency is preserved, optionally some Rules allow you to enable unmasking for users with the corresponding permissions by checking Permit unmasking of original values.
If Retain NULL values is checked, NULL values in the input will not be replaced/tokenized and will be retained as NULL in the output.