This repository contains the source code for the paper, "Maui: Black-Box Edge Privacy Attack on Graph Neural Networks" by Haoyu He, Isaiah J. King and H. Howie Huang, presented at PETS 2024.
[Paper]
@article{he2024maui,
title={Maui: Black-Box Edge Privacy Attack on Graph Neural Networks},
author={He, Haoyu and King, Isaiah J and Huang, H Howie},
journal={Proceedings on Privacy Enhancing Technologies},
volume={4},
pages={364--380},
year={2024}
}
We use Pytorch Geometric 2.4.0 (PyG) and later for implementation. Please follow the instruction to install PyG.
The datasets can be directly loaded from PyG.
We use configs.py to configure the experiment. Please check this file to see the complete list of options.
The trained models are saved in the ./src folder.
The matrices of influence nodes and influence values are saved in the ./influence_values folder, separated by datasets. They are saved as .npz files in coo format.
For small datasets, we run *.py. For large datasets, we run *_sample.py, which selects a random subgraph to attack in each round.
In the following sections, we provide simple commands for running the experiment on small datasets.
To train a target model, run the following:
python train.py --dataset DATASET_NAME --model MODEL_NAME
We provide GCN, GAT and GraphSAGE.
To use Maui, simply run:
python get_influence_values.py --dataset DATASET_NAME --model MODEL_NAME --attacker 0
To use Maui_comb, run:
python get_influence_values.py --dataset DATASET_NAME --model MODEL_NAME --attacker 0 --combo --combo_bar THRESHOLD
We use attacker to specify which attacker to use, specifically, 0: Maui, 1: LinkTeller, 2: LSA2.
eval_baseline.py can provide the all the evaluations from the paper. The command to run it is similar to running the attack, where you need to specify the dataset, model and the attack. Additionally, you can specify topk for precision and recall.
eval_k.py can provide precisions and recalls at different topk.
We also provide the evaluation for decoders in decode.py and decode_k.py.