The anon extension

The anon extension (PostgreSQL Anonymizer) provides data masking and anonymization capabilities to protect sensitive data in Postgres databases. It helps protect personally identifiable information (PII) and other sensitive data, facilitating compliance with regulations such as GDPR.

This guide introduces the anon extension and demonstrates how to implement masking rules for static data anonymization, which is currently the only supported masking method.

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Enable the extension

Enable the anon extension in your Neon database by following these steps:

1. Connect to your Neon database using either the Neon SQL Editor or an SQL client like psql

2. Enable experimental extensions:

   SET neon.allow_unstable_extensions='true';

3. Install the extension:

   CREATE EXTENSION IF NOT EXISTS anon;

Masking rules

Masking rules define which data to mask and how to mask it using SQL syntax. These rules are applied using SECURITY LABEL SQL commands and stored within the database schema to implement the privacy by design principle.

Masking functions

The anon extension provides built-in functions for different anonymization requirements, including but not limited to:

Static masking

Static masking permanently modifies the original data in your tables. This approach is useful for creating anonymized copies of data when:

• Migrating production data to development branches
• Creating sanitized datasets for testing
• Archiving data with sensitive information removed
• Distributing data to third parties

Implementation example

1. Create a sample table

   Create a sample users table with sensitive information:

   CREATE TABLE users (
       id SERIAL PRIMARY KEY,
       username VARCHAR(50) UNIQUE,
       email VARCHAR(255),
       phone_number VARCHAR(20),
       city VARCHAR(100)
   );
   
   INSERT INTO users (username, email, phone_number, city) VALUES
       ('john_doe', 'john.doe@example.com', '555-123-4567', 'New York'),
       ('jane_smith', 'jane.smith@example.com', '555-987-6543', 'Los Angeles'),
       ('peter_jones', 'peter.jones@example.com', '555-555-1111', 'Chicago');

2. Define masking rules

   Apply masking rules to specific columns using SECURITY LABEL:

   -- Mask email addresses with realistic-looking but fake emails
   SECURITY LABEL FOR anon ON COLUMN users.email
   IS 'MASKED WITH FUNCTION anon.dummy_safe_email()';
   
   -- Partially mask phone numbers, preserving the first digit and last two digits
   SECURITY LABEL FOR anon ON COLUMN users.phone_number
   IS 'MASKED WITH FUNCTION anon.partial(phone_number, 1, ''XXX-XXX-'', 2)';

3. Initialize and apply masking

   The anon.init() function initializes the anon extension by loading default fake data sets and setting up the masking environment. This required step prepares the database for anonymization operations and must be executed before applying any masking functions.

   SELECT anon.init();

   Then apply the masking rules to permanently transform the data:

   warning

   Static masking irreversibly modifies your data. The original values cannot be recovered after anonymization.

   SELECT anon.anonymize_table('users');

4. Verify results

   After applying the masking, your data will be anonymized according to the defined rules:

   SELECT * FROM users;

   id	username	email	phone_number	city
   1	john_doe	mcknightjulie@example.org	5XXX-XXX-67	New York
   2	jane_smith	davidhanson@example.org	5XXX-XXX-43	Los Angeles
   3	peter_jones	michael33@example.org	5XXX-XXX-11	Chicago

   Note how:

   • Email addresses were replaced with fictional but valid looking addresses
   • Phone numbers only show the first digit and last two digits
   • The username and city columns remain unchanged as no masking rules were defined for them

Limitations

• Neon currently only supports static masking with the anon extension
• Additional pg_catalog functions cannot be declared as TRUSTED in Neon's implementation

Conclusion

The anon extension provides a toolkit for protecting sensitive data in Postgres databases. By defining appropriate masking rules, you can create anonymized datasets that maintain usability while protecting individual privacy.

Reference

• PostgreSQL Anonymizer Repository
• Official Documentation
• Masking Functions Reference