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  1. Preface
  2. Introduction to Test Data Management
  3. Test Data Manager
  4. Projects
  5. Policies
  6. Data Discovery
  7. Data Subset
  8. Data Masking
  9. Data Masking Techniques and Parameters
  10. Data Generation
  11. Data Generation Techniques and Parameters
  12. Working with Test Data Warehouse
  13. Plans and Workflows
  14. Monitor
  15. Reports
  16. ilmcmd
  17. tdwcmd
  18. tdwquery
  19. Data Type Reference
  20. Data Type Reference for Test Data Warehouse
  21. Glossary

Shuffle Masking Parameters

Shuffle Masking Parameters

You can configure masking parameters to determine if shuffle masking is repeatable, the masking is repeatable for one workflow run, or the masking is random. You can also configure a lookup to ensure that replacement values originate from rows that contain specific values.
The following image shows Data Masking parameters that appear when you configure a Shuffle data masking rule:
The shuffle masking parameters are the random or representative shuffle type, seed number, and the constrained option.
The following table describes the parameters that you can configure for shuffle masking:
Parameter
Description
Shuffle Type
Select random or representative shuffling:
  • Random. Shuffle values from one row to another without checking if the target values are unique for each source value. For example, the Integration Service masks 12345 with 65432 in a row. The Integration Service can also replace 33333 with 12345 in another row.
  • Representative. All source rows with the same value receive the same shuffle value. When the Integration Service replaces 12345 with 65432, then it can use 65432 as a mask value for any row with a 12345 source value. Representative masking does not save values between workflow runs. Use repeatable masking to return the same values between workflow runs.
Seed
Starting point for creating repeatable output. Enter a number between 1 and 999. Default is 1.
Enabled when Representative Shuffle Type is selected.
Constrained
Restricts applying shuffle masking to rows that are constrained by another column. For example, shuffle employee names based on gender. Or, shuffle addresses within the same city. Choose the constraint column when you assign the rule to columns in a project.