- anon 0.10.0
- Data Anonymization for Postgres
Anonymization & Data Masking for PostgreSQL
postgresql_anonymizer is an extension to mask or replace
personally identifiable information (PII) or commercially sensitive data from
a PostgreSQL database.
The project relies on a declarative approach of anonymization. This means we're using the PostgreSQL Data Definition Language (DDL) in order to specify the anonymization strategy inside the table definition itself.
Once the masking rules are defined, you can access the anonymized data in 3 different ways :
- Anonymous Dumps : Simply export the masked data into an SQL file
- Static Masking : Remove permanently the PII according to the rules
- Dynamic Masking : Hide PII only for the masked users
In addition, various Masking Functions are available: randomization, faking, partial scrambling, shuffling, noise, or even your own custom function!
Read the Concepts section for more details and NEWS.md for information about the latest version.
Declaring The Masking Rules
The main idea of this extension is to offer anonymization by design.
The data masking rules should be written by the people who develop the application because they have the best knowledge of how the data model works. Therefore masking rules must be implemented directly inside the database schema.
This allows masking the data directly inside the PostgreSQL instance without using an external tool and thus limiting the exposure and the risks of data leak.
The data masking rules are declared simply by using security labels :
```sql =# CREATE EXTENSION IF NOT EXISTS anon CASCADE;
=# SELECT anon.load();
=# CREATE TABLE player( id SERIAL, name TEXT, points INT);
=# SECURITY LABEL FOR anon ON COLUMN player.name -# IS 'MASKED WITH FUNCTION anon.fake_last_name()';
=# SECURITY LABEL FOR anon ON COLUMN player.id -# IS 'MASKED WITH VALUE NULL'; ```
You can permanently remove the PII from a database with
anon.anonymize_database(). This will destroy the original data. Use with care.
```sql =# SELECT * FROM customer; id | full_name | birth | employer | zipcode | fk_shop -----+------------------+------------+---------------+---------+--------- 911 | Chuck Norris | 1940-03-10 | Texas Rangers | 75001 | 12 112 | David Hasselhoff | 1952-07-17 | Baywatch | 90001 | 423
=# CREATE EXTENSION IF NOT EXISTS anon CASCADE; =# SELECT anon.load();
=# SECURITY LABEL FOR anon ON COLUMN customer.full_name -# IS 'MASKED WITH FUNCTION anon.fake_first_name() || '' '' || anon.fake_last_name()';
=# SECURITY LABEL FOR anon ON COLUMN customer.birth -# IS 'MASKED WITH FUNCTION anon.random_date_between(''1920-01-01''::DATE,now())';
=# SECURITY LABEL FOR anon ON COLUMN customer.employer -# IS 'MASKED WITH FUNCTION anon.fake_company()';
=# SECURITY LABEL FOR anon ON COLUMN customer.zipcode -# IS 'MASKED WITH FUNCTION anon.random_zip()';
=# SELECT anon.anonymize_database();
=# SELECT * FROM customer; id | full_name | birth | employer | zipcode | fk_shop -----+-------------------+------------+------------------+---------+--------- 911 | michel Duffus | 1970-03-24 | Body Expressions | 63824 | 12 112 | andromach Tulip | 1921-03-24 | Dot Darcy | 38199 | 423
You can also use
anonymize_column() to remove data
from a subset of the database.
You can hide the PII from a role by declaring it as a "MASKED". Other roles will still access the original data.
=# SELECT * FROM people;
id | firstname | lastname | phone
T1 | Sarah | Conor | 0609110911
Step 1 : Activate the dynamic masking engine
=# CREATE EXTENSION IF NOT EXISTS anon CASCADE;
=# SELECT anon.start_dynamic_masking();
Step 2 : Declare a masked user
=# CREATE ROLE skynet LOGIN;
=# SECURITY LABEL FOR anon ON ROLE skynet IS 'MASKED';
Step 3 : Declare the masking rules
```sql =# SECURITY LABEL FOR anon ON COLUMN people.lastname -# IS 'MASKED WITH FUNCTION anon.fake_last_name()';
=# SECURITY LABEL FOR anon ON COLUMN people.phone -# IS 'MASKED WITH FUNCTION anon.partial(phone,2,$$**$$,2)'; ```
Step 4 : Connect with the masked user
=# \! psql peopledb -U skynet -c 'SELECT * FROM people;'
id | firstname | lastname | phone
T1 | Sarah | Stranahan | 06******11
Due to the core design of this extension, you cannot use
pg_dump with a masked
user. If you want to export the entire database with the anonymized data, you
must use the
pg_dump_anon command line. For example
pg_dump_anon -h localhost -p 5432 -U bob bob_db > dump.sql
For more details, please read the Anonymous Dumps section.
This project is still in beta phase and should be used carefully.
We need your feedback and ideas! Let us know what you think of this tool, how it fits your needs and what features are missing.
You can either open an issue or send a message at email@example.com.
This extension works with all supported versions of PostgreSQL.
It requires 2 extensions called tsm_system_rows and pgcrypto which are
delivered by the
postgresql-contrib package of the main linux distributions.
Step 1. Install the extension on the server with :
sudo pgxn install postgresql_anonymizer
Step 2: Load the extension in the database you want to anonymize
ALTER DATABASE foo SET session_preload_libraries = 'anon';
There are other ways to install and load the extension. You can read the INSTALL section for detailed instructions or if you want to deploy it on Amazon RDS or some other DBaaS provider.
The dynamic masking system only works with one schema (by default
public). When you start the masking engine with
start_dynamic_masking(), you can specify the schema that will be masked with
SELECT start_dynamic_masking('sales');. However static masking with
anon.anonymize()and Anonymous Dumps will work fine with multiple schemas.
The Anonymous Dumps may not be consistent. Use Static Masking combined with
pg_dumpif you can't fence off your database from
DDLcommands during the export.
So far, we've done very few performance tests. Depending on the size of your data set and number of columns your need to anonymize, you might end up with a very slow process.
Here's some ideas:
If you need to anonymize data for testing purpose, chances are that a smaller subset of your database will be enough. In that case, you can easily speed up the anonymization by downsizing the volume of data. There are multiple ways to extract a sample of database:
Dynamic masking is not always required! In some cases, it is more efficient to build Materialized Views instead.
CREATE MATERIALIZED VIEW masked_customer AS
anon.random_last_name() AS name,
anon.random_date_between('1920-01-01'::DATE,now()) AS birth,