membership_inference_attack

Implementation of the paper : "Membership Inference Attacks Against Machine Learning Models", Shokri et al.

I implement the most basic attack which assumes the adversary has the data which comes from the same distribution as the target model’s the training dataset. I choose to evaluate on MNIST, CIFAR10 and CIFAR100. I used the framework pytorch for the target and the shadow models and ligth gradient boosting for the attack model.

Tested on python 3.5 and torch 1.0

Congiguration

in the file config/config.yaml, you will find the different settings you can change.

Running experiements

By running main.py, you start the statistic proposed in statistics.type in the config.yaml. training_size will test all the values in training_size_value overfitting will test all the values in epoch_value number_shadow will test all the values in number_shadow_value