An anonymization tool for production databases
Project description
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 | dob | |
---|---|---|---|---|---|
1 | Dr. | Bernard | Gough | tnelson@powell.com | 2000-07-03 |
2 | Mr. | Molly | Bennett | clarkeharriet@price-fry.com | 2014-05-19 |
3 | Mrs. | Chelsea | Reid | adamsamber@clayton.com | 1974-09-08 |
4 | Dr. | Grace | Armstrong | tracy36@wilson-matthews.com | 1963-12-15 |
5 | Dr. | Stanley | James | christine15@stewart.net | 1976-09-16 |
6 | Dr. | Mark | Walsh | dgardner@ward.biz | 2004-08-28 |
7 | Mrs. | Josephine | Chambers | hperry@allen.com | 1916-04-04 |
8 | Dr. | Stephen | Thomas | thompsonheather@smith-stevens.com | 1995-04-17 |
9 | Ms. | Damian | Thompson | yjones@cox.biz | 2016-10-02 |
10 | Miss | Geraldine | Harris | porteralice@francis-patel.com | 1910-09-28 |
11 | Ms. | Gemma | Jones | mandylewis@patel-thomas.net | 1990-06-03 |
12 | Dr. | Glenn | Carr | garnervalerie@farrell-parsons.biz | 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
[...]
For a full list of data generation strategies, see the docs on strategyfiles
Process outline
- Restore from dumpfile to temporary database.
- Anonymize temporary database with strategy.
- Dump resulting data to file.
- 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- backup file in plain .sql/sql.gz (schema and data)
- Local or remote mysql >= 5.5
mssql
- Requires extra dependencies: install package
pynonymizer[mssql]
- MSSQL >= 2008
- Due to backup/restore limitations, you must be running pynonymizer on the same server as the database engine.
- A backup in
.bak
format
postgres
psql
/pg_dump
Must be in $PATH- backup file in plain .sql/sql.gz (schema and data)
- Local or remote postgres server
Getting Started
Usage
- Write a strategyfile for your database
- See below:
usage: pynonymizer [-h] [--input INPUT] [--strategy STRATEGYFILE]
[--output OUTPUT] [--db-type DB_TYPE] [--db-host DB_HOST]
[--db-name DB_NAME] [--db-user DB_USER]
[--db-password DB_PASSWORD] [--fake-locale FAKE_LOCALE]
[--start-at STEP] [--skip-steps STEP [STEP ...]]
[--stop-at STEP] [--seed-rows SEED_ROWS]
[--mssql-backup-compression] [-v]
A tool for writing better anonymization strategies for your production
databases.
optional arguments:
-h, --help show this help message and exit
--input INPUT, -i INPUT
The source dumpfile to read from. [$PYNONYMIZER_INPUT]
--strategy STRATEGYFILE, -s STRATEGYFILE
A strategyfile to use during anonymization.
[$PYNONYMIZER_STRATEGY]
--output OUTPUT, -o OUTPUT
The destination to write the dumped output to.
[$PYNONYMIZER_OUTPUT]
--db-type DB_TYPE, -t DB_TYPE
Type of database to interact with. More databases will
be supported in future versions. default: mysql
[$PYNONYMIZER_DB_TYPE]
--db-host DB_HOST, -d DB_HOST
Database hostname or IP address.
[$PYNONYMIZER_DB_HOST]
--db-name DB_NAME, -n DB_NAME
Name of database to restore and anonymize in. If not
provided, a unique name will be generated from the
strategy name. This will be dropped at the end of the
run. [$PYNONYMIZER_DB_NAME]
--db-user DB_USER, -u DB_USER
Database credentials: username. [$PYNONYMIZER_DB_USER]
--db-password DB_PASSWORD, -p DB_PASSWORD
Database credentials: password. Recommended: use
environment variables to avoid exposing secrets in
production environments. [$PYNONYMIZER_DB_PASSWORD]
--fake-locale FAKE_LOCALE, -l FAKE_LOCALE
Locale setting to initialize fake data generation.
Affects Names, addresses, formats, etc.
[$PYNONYMIZER_FAKE_LOCALE]
--start-at STEP Choose a step to begin the process (inclusive).
[$PYNONYMIZER_START_AT]
--skip-steps STEP [STEP ...]
Choose one or more steps to skip.
[$PYNONYMIZER_SKIP_STEPS]
--stop-at STEP Choose a step to stop at (inclusive).
[$PYNONYMIZER_STOP_AT]
--seed-rows SEED_ROWS
Specify a number of rows to populate the fake data
table used during anonymization.
[$PYNONYMIZER_SEED_ROWS]
--mssql-backup-compression
[MSSQL] Use compression when backing up the database.
[$PYNONYMIZER_MSSQL_BACKUP_COMPRESSION]
-v, --version show program's version number and exit
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