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.
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 | [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 |
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_emailcompanyfile_path[...]
For a full list of data generation strategies, see the docs on strategyfiles
You can see strategyfile examples for existing database, such as wordpress or adventureworks sample database, in the the examples folder.
- 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.
- Python >= 3.6
mysql/mysqldumpMust 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
- Requires extra dependencies: install package
pynonymizer[mssql] - MSSQL >= 2008
- For
RESTORE_DB/DUMP_DBoperations, the database server must be running locally with pynonymizer. This is because MSSQLRESTOREandBACKUPinstructions 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
psql/pg_dumpMust be in $PATH- Local or remote postgres server
- Supported Inputs:
- Plain SQL over stdout
- Plain SQL file
.sql - GZip-compressed SQL file
.gz
- Supported Outputs:
- Plain SQL over stdout
- Plain SQL file
.sql - GZip-compressed SQL file
.gz - LZMA-compressed SQL file
.xz
- Write a strategyfile for your database
- Start Anonymizing!
usage: pynonymizer [-h] [--input INPUT]
[--strategy STRATEGYFILE] [--output OUTPUT]
[--db-type DB_TYPE] [--db-host DB_HOST]
[--db-port DB_PORT] [--db-name DB_NAME]
[--db-user DB_USER]
[--db-password DB_PASSWORD]
[--fake-locale FAKE_LOCALE] [--start-at STEP]
[--only-step STEP]
[--skip-steps STEP [STEP ...]]
[--stop-at STEP] [--seed-rows SEED_ROWS]
[--mssql-driver MSSQL_DRIVER]
[--mssql-backup-compression]
[--mysql-cmd-opts MYSQL_CMD_OPTS]
[--mysql-dump-opts MYSQL_DUMP_OPTS]
[--postgres-cmd-opts POSTGRES_CMD_OPTS]
[--postgres-dump-opts POSTGRES_DUMP_OPTS]
[-v] [--verbose] [--dry-run]
A tool for writing better anonymization strategies for your
production databases.
optional arguments:
-h, --help show this help message and exit
--input INPUT, -i INPUT
The source dump filepath to read from.
Use `-` for stdin. [$PYNONYMIZER_INPUT]
--strategy STRATEGYFILE, -s STRATEGYFILE
A strategyfile to use during
anonymization. [$PYNONYMIZER_STRATEGY]
--output OUTPUT, -o OUTPUT
The destination filepath to write the
dumped output to. Use `-` for stdout.
[$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-port DB_PORT, -P DB_PORT
Database port. Defaults to provider
default. [$PYNONYMIZER_DB_PORT]
--db-name DB_NAME, -n DB_NAME
Name of database to restore and
anonymize in. If not provided, a unique
name will be generated from the strategy
name. This will be dropped at the end of
the run. [$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]
--only-step STEP Choose one step to perform.
[$PYNONYMIZER_ONLY_STEP]
--skip-steps STEP [STEP ...]
Choose one or more steps to skip.
[$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-driver MSSQL_DRIVER
[MSSQL] ODBC driver to use for database
connection [$PYNONYMIZER_MSSQL_DRIVER]
--mssql-backup-compression
[MSSQL] Use compression when backing up
the database.
[$PYNONYMIZER_MSSQL_BACKUP_COMPRESSION]
--mysql-cmd-opts MYSQL_CMD_OPTS
[MYSQL] pass additional arguments to the
restore process (advanced use only!).
[$PYNONYMIZER_MYSQL_CMD_OPTS]
--mysql-dump-opts MYSQL_DUMP_OPTS
[MYSQL] pass additional arguments to the
dump process (advanced use only!).
[$PYNONYMIZER_MYSQL_DUMP_OPTS]
--postgres-cmd-opts POSTGRES_CMD_OPTS
[POSTGRES] pass additional arguments to
the restore process (advanced use
only!). [$PYNONYMIZER_POSTGRES_CMD_OPTS]
--postgres-dump-opts POSTGRES_DUMP_OPTS
[POSTGRES] pass additional arguments to
the dump process (advanced use only!).
[$PYNONYMIZER_POSTGRES_DUMP_OPTS]
-v, --version show program's version number and exit
--verbose Increases the verbosity of the logging
feature, to help when troubleshooting
issues. [$PYNONYMIZER_VERBOSE]
--dry-run Instruct pynonymizer to skip all process
steps. Useful for testing safely.
[$PYNONYMIZER_DRY_RUN]
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" [...] )