Membership Inference Attacks to GNN for Graph Classification

The source code for ICDM2021 paper: "Adapting Membership Inference Attacks to GNN for Graph Classification: Approaches and Implications". The full version of the paper can be found in https://arxiv.org/abs/2110.08760

If you make use of this code in your work, please cite the following paper:

@inproceedings{wypy2021miagnn,
  title={Adapting Membership Inference Attacks to GNN for Graph Classification: Approaches and Implications},
  author={Bang, Wu and Xiangwen, Yang and Shirui, Pan and Xingliang, Yuan},
  booktitle={2021 IEEE International Conference on Data Mining (ICDM)},
  year={2021},
  organization={IEEE}
}

Installation

If you meet the version mismatch error for Lasagne library, please use following command to upgrade Lasagne library.

pip install --upgrade https://github.com/Lasagne/Lasagne/archive/master.zip

Usage

Step 1: Train Target and Shadow Model

To train the victim GCN model, use the following command.

For common graph dataset (DD, PROTEINS_full, ENZYMES):

sh run_TUs_target_shadow_training.sh --number 10 --start_epoch 100 --dataset DD

For graph converted via SuperPixel (CIFAR10, MNIST)

sh run_SPs_target_shadow_training.sh --number 10 --start_epoch 100 --dataset MNIST

--number this is the number of repeated model training

--start_epoch this is the minimum number of interactions to train the model

--dataset this is the dataset name of model training

For detailed code execution, you can refer to 'main_SPs_graph_classification.py' and 'main_TUs_graph_classification.py' in the ./code folder.

Step 2: Train Attack Model Inferring Graph Sample Membership

sh run_transfer_attach.sh --number 15

--number this is the number of repeated attack

For detailed code execution, you can refer to 'transfer_based_attack.py'.

Acknowledge

This project references from benchmarking-gnn and DeeperGCN

If you have any questions, please send an email to us.