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 (P...

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.

Enable the extension

When using the Neon Console or API for anonymization workflows, the extension is enabled automatically. It can also be enabled manually using SQL commands.

Enable via SQL

When working with SQL-based workflows (such as using psql or other SQL clients), enable the anon extension in your Neon database by following these steps:

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

Enable experimental extensions:

SQL

SET neon.allow_unstable_extensions='true';

Install the extension:
SQL
```
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

PostgreSQL Anonymizer provides built-in functions for different anonymization requirements, including but not limited to:

Function Type	Description	Example
Faking	Generate realistic data	`anon.fake_first_name()` and `anon.lorem_ipsum()`
Pseudonymization	Create consistent and reversible fake data	`anon.pseudo_email(seed)`
Randomization	Generate random values	`anon.random_int_between(10, 100)` and `anon.random_in_enum(enum_column)`
Partial scrambling	Hide portions of strings	`anon.partial(ip_address, 8, ''XXX.XXX'', 0)` would change `192.168.1.100` to `192.168.XXX.XXX`
Nullification	Replace with static values or `NULL`	`MASKED WITH VALUE 'CONFIDENTIAL'`
Noise addition	Alter numerical values while maintaining distribution	`anon.noise(salary, 0.1)` adds `+/- 10%` noise to the `salary` column
Generalization	Replace specific values with broader categories	`anon.generalize_int4range(age, 10)` would change `54` to `[50,60)`

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

Branch operations and static masking

When using Neon's branch features with static masking:

Creating a child branch copies all data as-is from the parent
Resetting a branch from the parent replaces all branch data with the parent's current state
In both cases, any previous anonymization is lost and must be reapplied

Practical examples

For complete implementation examples showing how to apply these masking functions in real workflows, see the data anonymization guide, which covers:

Creating and anonymizing development branches
Applying different masking strategies to protect sensitive data
Automating anonymization with GitHub Actions
Best practices and safety tips

Limitations

Neon currently only supports static masking with this extension
With static masking, branch reset operations restore original data, requiring anonymization to be run again
Additional pg_catalog functions cannot be declared as TRUSTED in Neon's implementation

Conclusion

This 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

Data anonymization workflow guide - Practical guide for anonymizing data in Neon branches
PostgreSQL Anonymizer Repository
Official Documentation
Masking Functions Reference

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