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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. Macro Transformation
  30. Match Transformation
  31. Match Transformations in Field Analysis
  32. Match Transformations in Identity Analysis
  33. Normalizer Transformation
  34. Merge Transformation
  35. Parser Transformation
  36. Python Transformation
  37. Rank Transformation
  38. Read Transformation
  39. Relational to Hierarchical Transformation
  40. REST Web Service Consumer Transformation
  41. Router Transformation
  42. Sequence Generator Transformation
  43. Sorter Transformation
  44. SQL Transformation
  45. Standardizer Transformation
  46. Union Transformation
  47. Update Strategy Transformation
  48. Web Service Consumer Transformation
  49. Parsing Web Service SOAP Messages
  50. Generating Web Service SOAP Messages
  51. Weighted Average Transformation
  52. Window Transformation
  53. Write Transformation
  54. Appendix A: Transformation Delimiters

Developer Transformation Guide

Developer Transformation Guide

Substitution Masking Properties

Substitution Masking Properties

You can configure the following masking rules for substitution masking:
  • Repeatable Output
    . Returns deterministic results between sessions. The Data Masking transformation stores masked values in the storage table.
  • Seed Value
    . Apply a seed value to generate deterministic masked data for a column. Enter a number between 1 and 1,000.
  • Unique Output
    . Force the Data Masking transformation to create unique output values for unique input values. No two input values are masked to the same output value. The dictionary must have enough unique rows to enable unique output.
    When you disable unique output, the Data Masking transformation might not mask input values to unique output values. The dictionary might contain fewer rows.
  • Unique Port
    . The port used to identify unique records for substitution masking. For example, you want to mask first names in a table called Customer. If you select the table column that contains the first names as the unique port, the data masking transformation replaces duplicate first names with the same masked value. If you select the Customer_ID column as the unique port, the data masking transformation replaces each first name with a unique value.
  • Optimize Dictionary Usage
    .
    Applicable if you select the
    Repeatable Output
    option. Increases the usage of masked values from a dictionary.
  • Dictionary Information
    . Configure the reference table that contains the substitute data values. Click
    Select Source
    to select a reference table.
    • Dictionary Name
      . Displays the name of the reference table that you select.
    • Dictionary Connection
      . Displays the name of the connection that contains the dictionary.
    • Serial Number Column
      . Choose the column to return to the Data Masking transformation.
    • Sort Column
      . The dictionary column on which you want to sort entries. Specify a sort column to generate deterministic results even if the order of entries in the dictionary changes. For example, if you move a relational dictionary and the order of entries changes, sort on the serial number column to consistently mask the data.
      The column that you choose must contain unique values. You cannot use a column that might contain duplicate values to sort the data.
    • Output Column
      . Choose the column to return to the Data Masking transformation.
  • Lookup condition.
    Configure a lookup condition to further qualify what dictionary row to use for substitution masking. The lookup condition is similar to the WHERE clause in an SQL query. When you configure a lookup condition you compare the value of a column in the source with a column in the dictionary.
    For example, you want to mask the first name. The source data and the dictionary have a first name column and a gender column. You can add a condition that each female first name is replaced with a female name from the dictionary. The lookup condition compares gender in the source to gender in the dictionary.
    • Input port
      . Source data column to use in the lookup.
    • Dictionary column
      . Dictionary column to compare the input port to.

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