Methods to mask PII data in PostgreSQL

Masking Personally Identifiable Information (PII) data in PostgreSQL involves various techniques to ensure data privacy while maintaining usability. 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 OR REPLACE 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 OR REPLACE VIEW masked_users AS

    SELECT

        user_id,

        CONCAT('555-', LPAD(FLOOR(RANDOM() * 1000000000)::text, 7, '0')) 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 COLUMN encrypted_ssn BYTEA;

    -- Encrypt SSN data

    UPDATE users

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

  

4: Hashing Method:

- Description: Convert sensitive data into irreversible hash values.

- Example: Hashing passwords for storage.

- Implementation:

   

    -- Create a hashed column for passwords

    ALTER TABLE users

    ADD COLUMN hashed_password VARCHAR(64);

    -- Hash passwords using SHA-256 algorithm

    UPDATE users

    SET hashed_password = crypt(password, gen_salt('sha256'));

  

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