qq-jiang/ethident-tifs

Behavior-aware Account De-anonymization on Ethereum Interaction Graph

This is a Python implementation of Ethident, as described in the following:

Behavior-aware Account De-anonymization on Ethereum Interaction Graph

Requirements

For hardware configuration, the experiments are conducted at Ubuntu 18.04.5 LTS with the Intel(R) Xeon(R) Gold 5218R CPU @ 2.10GHz, and NVIDIA Tesla V100S GPU (with 40GB memory each). For software configuration, all model are implemented in

• Python 3.7
• Pytorch-Geometric 2.0.3
• Pytorch 1.8.0
• Scikit-learn 0.24.1
• CUDA 10.2

Data

Download data from this page and place it under the 'data/' path.

Usage

Execute the following bash commands in the same directory where the code resides:

$ python main_ggc.py -l i --hop 2 -ess Volume -layer 2 --pooling max --hidden_dim 128 --batch_size 32 --lr 0.001 --dropout 0.2 -undir 1 --aug edgeRemove+identity --aug_prob1 0.1

More parameter settings can be found in 'utils/parameters.py'.

Citation

If you find this work useful, please cite the following:

@article{zhou2022behavior,
  title={Behavior-aware Account De-anonymization on Ethereum Interaction Graph},
  author={Zhou, Jiajun and Hu, Chenkai and Chi, Jianlei and Wu, Jiajing and Shen, Meng and Xuan, Qi},
  journal={arXiv preprint arXiv:2203.09360},
  year={2022}
}