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. 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

Masking Techniques

Masking Techniques

The masking technique is the type of data masking to apply to the selected column.
You can select one of the following masking techniques for an input column:
Random
Produces random, non-repeatable results for the same source data and masking rules. You can mask date, numeric, and string datatypes. Random masking does not require a seed value. The results of random masking are non-deterministic.
Expression
Applies an expression to a source column to create or mask data. You can mask all data types.
Key
Replaces source data with repeatable values. The Data Masking transformation produces deterministic results for the same source data, masking rules, and seed value. You can mask date, numeric, and string data types.
Substitution
Replaces a column of data with similar but unrelated data from a dictionary. You can mask the string data type.
Dependent
Replaces the values of one source column based on the values of another source column. You can mask the string data type.
Tokenization
Replaces source data with data generated based on customized masking criteria. The Data Masking transformation applies rules specified in a customized algorithm. You can mask the string data type.
Encryption
Replaces source data with encrypted values based on encryption criteria that you configure in the transformation. You can encrypt the string data type.
Special Mask Formats
Credit card number, email address, IP address, phone number, SSN, SIN, or URL. The Data Masking transformation applies built-in rules to intelligently mask these common types of sensitive data.
No Masking
The Data Masking transformation does not change the source data.
Default is No Masking.

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