Table of Contents

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  1. Preface
  2. Introduction to Transformations
  3. Transformation Ports
  4. Transformation Caches
  5. Address Validator Transformation
  6. Aggregator Transformation
  7. Association Transformation
  8. Bad Record Exception Transformation
  9. Case Converter Transformation
  10. Classifier Transformation
  11. Comparison Transformation
  12. Consolidation Transformation
  13. Data Masking Transformation
  14. Data Processor Transformation
  15. Decision Transformation
  16. Duplicate Record Exception Transformation
  17. Expression Transformation
  18. Filter Transformation
  19. Hierarchical to Relational Transformation
  20. Java Transformation
  21. Java Transformation API Reference
  22. Java Expressions
  23. Joiner Transformation
  24. Key Generator Transformation
  25. Labeler Transformation
  26. Lookup Transformation
  27. Lookup Caches
  28. Dynamic Lookup Cache
  29. Match Transformation
  30. Match Transformations in Field Analysis
  31. Match Transformations in Identity Analysis
  32. Normalizer Transformation
  33. Merge Transformation
  34. Parser Transformation
  35. Python Transformation
  36. Rank Transformation
  37. Read Transformation
  38. Relational to Hierarchical Transformation
  39. REST Web Service Consumer Transformation
  40. Router Transformation
  41. Sequence Generator Transformation
  42. Sorter Transformation
  43. SQL Transformation
  44. Standardizer Transformation
  45. Union Transformation
  46. Update Strategy Transformation
  47. Web Service Consumer Transformation
  48. Parsing Web Service SOAP Messages
  49. Generating Web Service SOAP Messages
  50. Weighted Average Transformation
  51. Window Transformation
  52. Write Transformation
  53. Appendix A: Transformation Delimiters

Developer Transformation Guide

Developer 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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