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.
note
This extension comes from the PostgreSQL Anonymizer open source project (postgresql_anonymizer). This is distinct from other tools such as pg_anon. The extension is installed using CREATE EXTENSION anon.
Looking for a practical guide?
For complete step-by-step workflows on anonymizing data in Neon branches, including manual procedures and GitHub Actions automation, see data anonymization.
Enable the extension
note
This extension is currently experimental and may change in future releases.
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:
SET neon.allow_unstable_extensions='true'; -
Install the extension:
CREATE EXTENSION IF NOT EXISTS anon;
tip
When using the Neon Console or API to create branches, the extension is enabled automatically. See the data anonymization workflow guide for details.
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_catalogfunctions cannot be declared asTRUSTEDin 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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