Here are a few common data masking techniques you can use to protect sensitive data within your datasets.
1. Data Pseudonymization
Lets you switch an original data set, such as a name or an e-mail, with a pseudonym or an alias. This process is reversible—it de-identifies data yet still enables later use of re-identification if needed.
2. Data Anonymization
A method that lets you encode identifiers that connect individuals to the masked data. The goal is to protect the private activity of users while preserving the credibility of the masked data.
3. Lookup substitution
You can mask a production database with an added lookup table that provides alternative values to the original, sensitive data. This allows you to use realistic data in a test environment, without exposing the original.
4. Encryption
Lookup tables are easily compromised, so it is recommended you encrypt data so that it can only be accessed via a password. The data is unreadable while encrypted, but is viewable when decrypted, so you should combine this with other data masking techniques.
5. Redaction
If the sensitive data is not necessary for QA or development purposes, you can replace it with generic values in the development and testing environment. In this case there is no realistic data with similar attributes to the original.
6. Averaging
If you want to reflect sensitive data in terms of averages or aggregates, but not on an individual basis, you can replace all the values in the table with the average value. For example, if the table lists employee salaries, you can mask the actual individual salaries by replacing them all with the average salary, so the overall column matches the real overall value of the combined salaries.
7. Shuffling
If you need to retain uniqueness when masking values, you can protect the data by scrambling it, so that the real values remain, but are assigned to different elements. Given the salary table example, the actual salaries will all be listed, but it won’t be revealed which salary belongs to each employee. This method is best suited to larger datasets.
This may help you,
Rachel Gomez
