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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:

<pre> @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} } </pre>

Installation

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

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

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):

<pre> sh run_TUs_target_shadow_training.sh --number 10 --start_epoch 100 --dataset DD </pre>

For graph converted via SuperPixel (CIFAR10, MNIST)

<pre> sh run_SPs_target_shadow_training.sh --number 10 --start_epoch 100 --dataset MNIST </pre>

--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

<pre> sh run_transfer_attach.sh --number 15 </pre>

--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.