Post by jabom on Dec 28, 2023 5:55:06 GMT -5
There are many techniques available for implementing data masking, such as: Shuffling Shuffling involves permuting the elements within columnar data to ensure no correlation between them. For example, if the values are from through , then shuffling would mean that the rows would be arranged in random order. Blurring Blurring involves hiding fields within rows by applying noise functions such as Gaussian Blur or Median Filter.
This technique does not change the total number Job Function Email List of columns or rows but does change their values. However, it does not provide significant protection against correlation attacks because noise functions are easy to reverse engineer using statistical analysis techniques like linear regression analysis. Substitution The sensitive data is replaced with a placeholder value (such as a sequence number) that doesn’t reveal any information about the original data. For example, credit card numbers in financial services could be masked with meaningless numbers that can’t be traced back to actual cardholders.
Tokenization Tokenization replaces one piece of sensitive data with another that has no value in and of itself but can be recognized by an application as belonging to a particular category. For example, bank account numbers might be replaced with random tokens rather than actual account numbers. Character Scrambling The sensitive data is scrambled so that it cannot be reversed back into its original form. Data Masking Examples – ! Masking sensitive data protects against data security threats by: Protects Against Data Security.
This technique does not change the total number Job Function Email List of columns or rows but does change their values. However, it does not provide significant protection against correlation attacks because noise functions are easy to reverse engineer using statistical analysis techniques like linear regression analysis. Substitution The sensitive data is replaced with a placeholder value (such as a sequence number) that doesn’t reveal any information about the original data. For example, credit card numbers in financial services could be masked with meaningless numbers that can’t be traced back to actual cardholders.
Tokenization Tokenization replaces one piece of sensitive data with another that has no value in and of itself but can be recognized by an application as belonging to a particular category. For example, bank account numbers might be replaced with random tokens rather than actual account numbers. Character Scrambling The sensitive data is scrambled so that it cannot be reversed back into its original form. Data Masking Examples – ! Masking sensitive data protects against data security threats by: Protects Against Data Security.