PATE Example

The scripts in the folder allow you to train a MNIST model using PATE diffrential privacy framework. While running this example would give you an accurate implementation of a PATE implementation. An accurate analysis of DP guarantees is still an work in progress.

[Sorry, I wasn't a very good coder when I wrote this. But, not maintaining this anymore]

Requirements:

• Pytorch
• PySyft

$ python Main.py

Scripts present

• data: Consists of functions for loading datasets
• Main: The file to be run for a complete PATE model
• Model: PyTorch model definition. The same model is used for student and teacher.
• Student: Class to handle student functionality such as training and making predictions
• Teacher: Class to handle teacher functionality such as training and making noisy predictions. All the Teacher ensembles are handled in this Class
• util: Functions

This training loop is then executed for a given number of epochs. The performance on the test set of MNIST is shown after each epoch.