pynonymizer

pynonymizer is a universal tool for translating sensitive production database dumps into anonymized copies.

This can help you support GDPR/Data Protection in your organization without compromizing on quality testing data.

Why are anonymized databases important?

The primary source of information on how your database is used is in your production database. In most situations, the production dataset is usually significantly larger than any development copy, and would contain a wider range of data.

From time to time, it is prudent to run a new feature or stage a test against this dataset, rather than one that is artificially created by developers or by testing frameworks. Anonymized databases allow us to use the structures present in production, while stripping them of any personally identifiable data that would consitute a breach of privacy for end-users and subsequently a breach of GDPR.

With Anonymized databases, copies can be processed regularly, and distributed easily, leaving your developers and testers with a rich source of information on the volume and general makeup of the system in production. It can be used to run better staging environments, integration tests, and even simulate database migrations.

below is an excerpt from an anonymized database:

id	salutation	firstname	surname	email	dob
1	Dr.	Bernard	Gough	[email protected]	2000-07-03
2	Mr.	Molly	Bennett	[email protected]	2014-05-19
3	Mrs.	Chelsea	Reid	[email protected]	1974-09-08
4	Dr.	Grace	Armstrong	[email protected]	1963-12-15
5	Dr.	Stanley	James	[email protected]	1976-09-16
6	Dr.	Mark	Walsh	[email protected]	2004-08-28
7	Mrs.	Josephine	Chambers	[email protected]	1916-04-04
8	Dr.	Stephen	Thomas	[email protected]	1995-04-17
9	Ms.	Damian	Thompson	[email protected]	2016-10-02
10	Miss	Geraldine	Harris	[email protected]	1910-09-28
11	Ms.	Gemma	Jones	[email protected]	1990-06-03
12	Dr.	Glenn	Carr	[email protected]	1998-04-19

How does it work?

pynonymizer replaces personally identifiable data in your database with realistic pseudorandom data, from the Faker library or from other functions. There are a wide variety of data types available which should suit the column in question, for example:

• unique_email
• company
• file_path
• [...]

Pynonymizer's main data replacement mechanism fake_update is a random selection from a small pool of data (--seed-rows controls the available Faker data). This process is chosen for compatibility and speed of operation, but does not guarantee uniqueness. This may or may not suit your exact use-case. For a full list of data generation strategies, see the docs on strategyfiles

Examples

You can see strategyfile examples for existing database, such as wordpress or adventureworks sample database, in the the examples folder.

Process outline

1. Restore from dumpfile to temporary database.
2. Anonymize temporary database with strategy.
3. Dump resulting data to file.
4. Drop temporary database.

If this workflow doesnt work for you, see process control to see if it can be adjusted to suit your needs.

Requirements

• Python >= 3.6

mysql

• mysql/mysqldump Must be in $PATH
• Local or remote mysql >= 5.5
• Supported Inputs:
  • Plain SQL over stdout
  • Plain SQL file .sql
  • GZip-compressed SQL file .gz
• Supported Outputs:
  • Plain SQL over stdout
  • Plain SQL file .sql
  • GZip-compressed SQL file .gz
  • LZMA-compressed SQL file .xz

mssql

• Requires extra dependencies: install package pynonymizer[mssql]
• MSSQL >= 2008
• For RESTORE_DB/DUMP_DB operations, the database server must be running locally with pynonymizer. This is because MSSQL RESTORE and BACKUP instructions are received by the database, so piping a local backup to a remote server is not possible.
• The anonymize process can be performed on remote servers, but you are responsible for creating/managing the target database.
• Supported Inputs:
  • Local backup file
• Supported Outputs:
  • Local backup file

postgres

• psql/pg_dump Must be in $PATH
• Local or remote postgres server
• Supported Inputs:
  • Plain SQL over stdout
  • Plain SQL file .sql
  • GZip-compressed SQL file .gz
• Supported Outputs:
  • Plain SQL over stdout
  • Plain SQL file .sql
  • GZip-compressed SQL file .gz
  • LZMA-compressed SQL file .xz

Getting Started

Usage

CLI

1. Write a strategyfile for your database
2. Check out the help for a description of options pynonymizer --help
3. Start Anonymizing!

Package

Pynonymizer can also be invoked programmatically / from other python code. See the module entrypoint pynonymizer or pynonymizer/pynonymize.py

import pynonymizer

pynonymizer.run(input_path="./backup.sql", strategyfile_path="./strategy.yml" [...] )