Deployment

• git clone or download from here
• create a virtual environment
• Install requirements: pip install requirements.txt
• Create a superuser: python manage.py createsuperuser
• Start the development server: python manage.py runserver
• Login to the interface with the account you created

API

Endpoint URL

<server url>: anonymizer/api/<secret key>/<parameters>

• secret key: The secret access key generated for the user through the Anonymiser’s user interface.
• parameters: Parameters for the data to be returned, including limit, offset, filters on properties and count.

Usage

The first screen of the tool allows you to setup or edit an existing, saved configuration. Each configuration is basically a separate anonymization project to various database back-ends or text files, with different executed anonymization functions. By creating a new configuration the system will prompt the user to connect to the private database backend or select the file to open and select the entities / tables to anonymise.

https://github.com/aegisbigdata/documentation/blob/master/tutorials/anonymiser-screenshots/anon1.jpg

The system then prompts the user to select which fields from the data source to expose to the anonymised set, as well as the anonymisation function to be performed.

https://github.com/aegisbigdata/documentation/blob/master/tutorials/anonymiser-screenshots/anon2.png

The anonymisation system comes with a list of predefined anonymisation functions that can be used directly (e.g. city from an exact address, range of values from an integer), as well as a list of aggregation functions (e.g. average).

https://github.com/aegisbigdata/documentation/blob/master/tutorials/anonymiser-screenshots/anon3.png

The tool can be easily extended with any new, custom anonymisation functions defined by the user in a python module.

https://github.com/aegisbigdata/documentation/blob/master/tutorials/anonymiser-screenshots/anon4.png

The user is able to execute queries and test the anonymised output through the integrated console of the tool as seen in the following screenshot.

https://github.com/aegisbigdata/documentation/blob/master/tutorials/anonymiser-screenshots/anon5.png

The anonymised (output) data can be exposed through API to external parties in a secure way through the provision of access keys.

https://github.com/aegisbigdata/documentation/blob/master/tutorials/anonymiser-screenshots/anon6.png

Users can access the anonymised data in JSON format through API provided by the anonymization tool using their private access keys.

https://github.com/aegisbigdata/documentation/blob/master/tutorials/anonymiser-screenshots/anon7.png