The answer above refers to Data Masking, which is a technique that allows to protect sensitive data but have an unreadable data that resembles the shape/format of the original data. It is often used to mask out sensitive data when testing, developing, or training.
How Data Masking Works
Data masking is a process which not only implements various means but also replace sensitive information with a substituted non-sensitive, but a realistic version of data. Here are some common methods:
Data Substition:
Substituting sensitive data elements with each other with random or pseudo random values.
Like changing a real name to a generic name such as “John Doe”
Data Shuffling:
Reordering items within a field
A common example would be when digits of a credit card number are shuffled.
Data Encryption:
Scrambling sensitive information so that it cannot be read without the sensitive data from the decryption key
But this technique can do harm to the usability and the performance of your data.
Format-Preserving Encryption (FPE):
This form of encryption keeps the format of the original data intact.
This is best for when, data masking is needed, while retaining the structure and usability.
Benefits of Data Masking
Feature: Data Security — It safeguards private data against unauthorised access and abuse.
Compliance: Allows organizations to meet the requirements of data privacy regulations such as GDPR and CCPA.
Test and Development: Allows to create test and development environments without exposing actual data.
Training and Education: It offers realistic training data with no privacy issues.
Challenges and Considerations
Data Utility: If masking is performed in an aggressive manner then it will decreases the utility of masked data while testing/analysis ചെയ്യുവാനുവാകം.
Difficulty: To appropriately mask the data, additional planning and technological know-how are required.
Risk of Re-identification: Data masking can be done in a wrong way which leads to exposure of the entire data in case of its need for processing, exposing the sensitive information which leads to reidentification of sensitive information.
Data Masking : Best practices
Step #1: Determine Sensitive Data: Identify what data you want to mask
Select Masking Methodologies: Select methods that offer an effective balance between data confidentiality and utility
Keep The Data Usable: Make sure the data can be referred to after being masked.
Monitoring and updating the masking techniques according to the threats evolved, and regulatory compliance evolved as well.
With proper data masking, companies can safeguard their protected data from getting leaked, mitigate risks, and abide by the terms of data privacy laws. bbb b b
