Hi, I'm Ask INFA!
What would you like to know?
ASK INFAPreview
Please to access Ask INFA.

Table of Contents

Search

  1. Preface
  2. Transformations
  3. Source transformation
  4. Target transformation
  5. Access Policy transformation
  6. Aggregator transformation
  7. B2B transformation
  8. Chunking transformation
  9. Cleanse transformation
  10. Data Masking transformation
  11. Data Services transformation
  12. Deduplicate transformation
  13. Expression transformation
  14. Filter transformation
  15. Hierarchy Builder transformation
  16. Hierarchy Parser transformation
  17. Hierarchy Processor transformation
  18. Input transformation
  19. Java transformation
  20. Java transformation API reference
  21. Joiner transformation
  22. Labeler transformation
  23. Lookup transformation
  24. Machine Learning transformation
  25. Mapplet transformation
  26. Normalizer transformation
  27. Output transformation
  28. Parse transformation
  29. Python transformation
  30. Rank transformation
  31. Router transformation
  32. Rule Specification transformation
  33. Sequence transformation
  34. Sorter transformation
  35. SQL transformation
  36. Structure Parser transformation
  37. Transaction Control transformation
  38. Union transformation
  39. Vector Embedding transformation
  40. Velocity transformation
  41. Verifier transformation
  42. Web Services transformation

Transformations

Transformations

Random masking

Random masking

Random masking generates random nondeterministic masked data.
The Data Masking transformation returns different values when the same source value occurs in different rows. You can configure masking rules that affect the format of data that the Data Masking transformation returns.
You can mask datetime, numeric, and string values with random masking.
To mask date values with random masking, either configure a range of output dates or choose a variance. When you configure a variance, choose a part of the date to blur. Choose the year, month, day, hour, minute, or second. The Data Masking transformation returns a date that is within the range you configure.
When you mask numeric data, you can configure a range of output values for a column. The Data Masking transformation returns a value between the minimum and maximum values of the range based on field precision. To define the range, configure the minimum and maximum ranges or configure a blurring range based on a variance from the original source value.
Configure random masking to generate random output for string columns. To configure limitations for each character in the output string, configure a mask format. Configure source and target filter characters to define which source characters to mask and the characters to mask them with.

0 COMMENTS

We’d like to hear from you!