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App to anonymize data in Django models.

Project description



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, django-eadred 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 anonymizers


./ test anonymizers.tests

Version 0.5.1

  • Added unicode compatibility for files

  • Now with universal wheels

  • Added a new maintainer

Version 0.5

  • Python 3 compatibility

Version 0.4

  • Changed ‘Anonymizer.attributes’ to require every field to be listed. This is deal with the common security problem when a model is updated, but the Anonymizer is not updated.

    Fields that should not be anonymized should specify the special value “SKIP” as the ‘replacer’.

  • attributes must now be a list of tuples, not a dictionary.

Version 0.3

  • Support for DecimalField.

  • Improved docs.

  • Added tests

Version 0.2

  • Changed format of attributes from a dictionary to a list of 2-tuples (with backwards compatibility - previous format is deprecated).

  • Fixed small bug with names/usernames/emails sometimes not being generated in corresponding sets, due to fields with unique=True not being (reliably) set before other fields.

  • Added docs.

Version 0.1.2

  • Changed ‘varchar’ field to do max_length introspection at runtime. This breaks the previous signature of the function.

  • Introduced ‘replacers’ module and new shortcuts.

Version 0.1.1

  • Removed some debug code

  • Better handling of SlugField and skipped fields in introspection

Version 0.1

Initial release

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