How to decide on the Data Masking Techniques?

Which Data Masking features will be used to the information? It is a tough question to answer. It is an essential step to decide on the data masking techniques. Though data masking has several data manipulation capabilities, not all are suitable to uphold official business contextual information. These capabilities include:

  •  Non-deterministic randomization: Substituting a receptive field with a randomly generated value subject to various limitations to make sure that the data is still valid like not giving February 30 days. For example-Altering the date from 12/31/2011 to 01/05/2012.
  • Blurring: It is done by adding a random variance to the original value; for instance, restoring a savings account value with a random value but within an 8% range of the original.
  • Nulling: In this we replace a value with a null symbol: For example, replacing a Social Security number of 202-40-8568 with ###-##-8568.
  • Shuffling: The order of value is shuffled, such as changing a zip code of 23456 to 53246.
  • Repeatable masking: In this technique we maintain referential integrity by generating values that are both repeatable and only one of its kind. For instance, the tax id number 32-2498687 is replaced with 34-2435980 regularly.
  • Substituting the Values: In this we randomly substitute original values with the help of a substitution table of values, For example- replacing “Jack Doe” with “Rose Smith” from a list of 200,000 given names and surnames.
  • Tokenization: It is a unique form of data masking where we maintain the algorithm used to mask the data so the data can be restored to its original value later.
  • Specialized rules: These rules are used for particular fields such as Social Security/tax id numbers, credit card numbers, street addresses and telephone numbers that are structurally accurate and used for workflow and checksum validation.

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