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Table of Contents

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  1. Preface
  2. Working with Transformations
  3. Address Validator Transformation
  4. Aggregator Transformation
  5. Association Transformation
  6. Bad Record Exception Transformation
  7. Case Converter Transformation
  8. Classifier Transformation
  9. Cleanse transformation
  10. Comparison Transformation
  11. Custom Transformation
  12. Custom Transformation Functions
  13. Consolidation Transformation
  14. Data Masking Transformation
  15. Data Masking Examples
  16. Decision Transformation
  17. Duplicate Record Exception Transformation
  18. Dynamic Lookup Cache
  19. Expression Transformation
  20. External Procedure Transformation
  21. Filter Transformation
  22. HTTP Transformation
  23. Identity Resolution Transformation
  24. Java Transformation
  25. Java Transformation API Reference
  26. Java Expressions
  27. Java Transformation Example
  28. Joiner Transformation
  29. Key Generator Transformation
  30. Labeler Transformation
  31. Lookup Transformation
  32. Lookup Caches
  33. Match Transformation
  34. Match Transformations in Field Analysis
  35. Match Transformations in Identity Analysis
  36. Merge Transformation
  37. Normalizer Transformation
  38. Parser Transformation
  39. Rank Transformation
  40. Router Transformation
  41. Sequence Generator Transformation
  42. Sorter Transformation
  43. Source Qualifier Transformation
  44. SQL Transformation
  45. Using the SQL Transformation in a Mapping
  46. Stored Procedure Transformation
  47. Standardizer Transformation
  48. Transaction Control Transformation
  49. Union Transformation
  50. Unstructured Data Transformation
  51. Update Strategy Transformation
  52. Weighted Average Transformation
  53. XML Transformations

Transformation Guide

Transformation Guide

Dependent Masking

Dependent Masking

Dependent masking substitutes multiple columns of source data with data from the same dictionary row.
When the Data Masking transformation performs substitution masking for multiple columns, the masked data might contain unrealistic combinations of fields. You can configure dependent masking in order to substitute data for multiple input columns from the same dictionary row. The masked data receives valid combinations such as, "New York, New York" or "Chicago, Illinois."
When you configure dependent masking, you first configure an input column for substitution masking. Configure other input columns to be dependent on that substitution column. For example, you choose the ZIP code column for substitution masking and choose city and state columns to be dependent on the ZIP code column. Dependent masking ensures that the substituted city and state values are valid for the substituted ZIP code value.
You cannot configure a column for dependent masking without first configuring a column for substitution masking.
Configure the following masking rules when you configure a column for dependent masking:
Dependent column
The name of the input column that you configured for substitution masking. The Data Masking transformation retrieves substitute data from a dictionary using the masking rules for that column. The column you configure for substitution masking becomes the key column for retrieving masked data from the dictionary.
Output column
The name of the dictionary column that contains the value for the column you are configuring with dependent masking.

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