Table of Contents

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  1. Preface
  2. Introduction to Test Data Management
  3. Test Data Manager
  4. Projects
  5. Policies
  6. Data Discovery
  7. Creating a Data Subset
  8. Performing a Data Masking Operation
  9. Data Masking Techniques and Parameters
  10. Data Generation
  11. Data Generation Techniques and Parameters
  12. Working with Test Data Warehouse
  13. Analyzing Test Data with Data Coverage
  14. Plans and Workflows
  15. Monitor
  16. Reports
  17. ilmcmd
  18. tdwcmd
  19. tdwquery
  20. Appendix A: Data Type Reference
  21. Appendix B: Data Type Reference for Test Data Warehouse
  22. Appendix C: Data Type Reference for Hadoop
  23. Appendix D: Glossary

Data Masking Rules

Data Masking Rules

A data masking rule is a data masking technique to mask a specific type of data. You can create a standard rule, advanced rule, or a rule that you import as a mapplet.
A data masking technique defines the logic that masks the data. Masking parameters are options that you configure for a masking technique. For example, you can define different dictionary files for substitution masking rules. Dictionary files contain the sample data for substitution. You might blur output results by different percentages for different columns. Most masking techniques have associated masking parameters.
You can enable users to override masking parameters for a rule. For example, you create a rule with the substitution masking technique to mask column data based on a flat file substitution source. You set the override option for the rule. When a developer assigns this rule to columns in a source, the developer can select a relational database as a substitution source rather than a flat file.
You can assign rules to source columns, data domains, policies, and plans.