If you want to get started in Differential Privacy, go to the Getting Started folder (https://github.com/dhasuda/Differential-Privacy-Lab/tree/master/Getting%20Started), we managed to document and explain the basics of DP there!
If you already master the basics of DP and just want to implement your own experiments using this Lab, you can follow the steps below to setup everything. I hope it is useful for you!
There is a script setup.sh that can help you with that. It creates a brand-new environment and install all the dependencies needed for you to run the Differential Privacy Lab. Simply run the script in the terminal
sh setup.sh
The setup only needs to be done once
After the setup is complete at least once, you can run the following commands from the root directory of this repo:
source venv/bin/activate
cd code/
jupyter notebook
This will open a new window or tab in your browser of choice. From there, all the available directories to have access to all notebooks implemented!
This is part of the graduation project of Davi Grossi Hasuda about differential privacy for ITA (Instituto Tecnológico de Aeronáutica).
In this project, the Laplacian Mechanism to keep data private is implemented and analyzed when using some of the most common AI algorithms, such as Decision Tree and Machine Learning.
This project is oriented by Juliana de Melo Bezerra (http://www.comp.ita.br/~juliana/)
In this repo there is a more refined and easier to understand version of the original repo used during most of the project development, which you can find here: https://github.com/dhasuda/tg-differential-privacy