Methods to mask PII data in MSSQL

Masking Personally Identifiable Information (PII) data in Microsoft SQL Server (MSSQL) databases involves similar techniques to MySQL and PostgreSQL. Let's explore some methods with a real-life example and database implementation:

Method 1: Substitution

- Description: Replace sensitive data with fictitious values.

- Example: Masking email addresses by replacing the domain with a generic one.

- Implementation:

    

    -- Create a view to mask email addresses

    CREATE VIEW masked_users AS

    SELECT

        user_id,

        CONCAT('user', user_id, '@example.com') AS masked_email,

        first_name,

        last_name

    FROM

        users;

    

Method 2: Randomization

- Description: Generate random values to replace sensitive data.

- Example: Masking phone numbers by replacing digits with random numbers.

- Implementation:

  

    -- Create a view to mask phone numbers

    CREATE VIEW masked_users AS

    SELECT

        user_id,

        CONCAT('555-', RIGHT(1000000000 + FLOOR(RAND() * 1000000000), 7)) AS masked_phone,

        first_name,

        last_name

    FROM

        users;

 

Method 3: Encryption

- Description: Encrypt sensitive data using cryptographic algorithms.

- Example: Encrypting social security numbers.

- Implementation:

  

    -- Create an encrypted column for SSN

    ALTER TABLE users

    ADD encrypted_ssn VARBINARY(256);

    -- Encrypt SSN data

    UPDATE users

    SET encrypted_ssn = ENCRYPTBYPASSPHRASE('encryption_key', ssn);

  

Method 4: Hashing

- Description: Convert sensitive data into irreversible hash values.

- Example: Hashing passwords for storage.

- Implementation:

   

    -- Create a hashed column for passwords

    ALTER TABLE users

    ADD hashed_password CHAR(64);

    -- Hash passwords using SHA-256 algorithm

    UPDATE users

    SET hashed_password = CONVERT(VARCHAR(64), HASHBYTES('SHA2_256', CONCAT(salt, password)), 2);

  

These methods provide organizations with options to mask PII data in MSSQL databases, ensuring compliance with data privacy regulations while maintaining data integrity and usability. Adjust the techniques according to specific requirements and privacy policies.