Django Anonymizer


This project is not active.

The approach taken by this project doesn't scale well above small databases - it requires a very large number of database SELECTs and UPDATEs.

Also, the method envisaged by this project assumes you are running it on your development machine. However, that means that you have already downloaded customer data onto your machine, which is an issue.

I therefore recommend that an anonymization script should use SQL directly, and should be run in a specially prepared database before the data gets anywhere near developer machines.

For this reason, there won't be further work on this project.



This app helps you anonymize data in a database used for development of a Django project.

It is common practice in develpment to use a database that is very similar in content to the real data. The problem is that this can lead to having copies of sensitive customer data on development machines. This Django app helps by providing an easy and customizable way to anonymize data in your models.

The basic method is to go through all the models that you specify, and generate fake data for all the fields specified. Introspection of the models will produce an anonymizer that will attempt to provide sensible fake data for each field, leaving you to tweak for your needs.

Please note that the methods provided may not be able to give full anonymity. Even if you anonymize the names and other details of your customers, there may well be enough data to identify them. Relationships between records in the database are not altered, in order to preserve the characteristic structure of data in your application, but this may leave you open to information leaks which might not be acceptable for your data. This application should be good enough for simpler policies like 'remove all real telephone numbers from the database'.

An alternative approach to the problem of realistic amounts of test data for development/tests is to populate a database from scratch - see django-poseur, django-mockups and django-autofixture. The disavantage of that method is that the structure of the data - in terms of related models - can be unrealistic.


Quick overview (see docs for more information, either in docs/ or on <>).

  • Install using or pip/easy_install.

  • Add 'anonymizer' to your INSTALLED_APPS setting.

  • Create some stub files for your anonymizers:

    ./ create_anonymizers app_name1 [app_name2...]

    This will create a file in each of the apps you specify. (It will not overwrite existing files).

  • Edit the generated files, adjusting or deleting as necessary, using the functions in module anonymizer.replacers or custom functions.

  • Run the anonymizers:

    ./ anonymize_data app_name1 [app_name2...]

    This will DESTRUCTIVELY UPDATE all your data. Make sure you only do this on a copy of your database, use at own risk, yada yada.

  • Note: your database may not actually delete the changed data from the disk when you update fields. For Postgresql you will need to VACUUM FULL to delete that data.

    And even then, your operating system may not delete the data from the disk. Properly getting rid of these traces is left as an excercise to the reader :-)


To run the test suite, do the following inside the folder containing this README: test --settings=anonymizer.test_settings