Blink: Link Local Differential Privacy in Graph Neural Networks via Bayesian Estimation

This is the code repository for our paper "Blink: Link Local Differential Privacy in Graph Neural Networks via Bayesian Estimation" to appear in CCS '23. You can read the preprint paper here.

Dependencies

To run the experiments in this repo, you need numpy, matplotlib, sklearn, torch, torch_sparse, torch_geometric. You can install all the dependencies is through conda and pip (please use the CUDA version applicable to your system):

conda install pytorch pytorch-cuda=11.7 -c pytorch -c nvidia
pip3 install torch_geometric
pip3 install torch-scatter torch-sparse -f https://data.pyg.org/whl/torch-2.0.0+cu117.html
pip3 install scikit-learn

File structure

• ./src: the source directory of all the mechanisms, datasets and models we have experimented with.
  • ./src/blink implements our main result, the Blink framework.
  • ./src/rr implements vanilla randomized response as a baseline.
  • ./src/ldpgcn implement a LDP variant of DPGCN from Wu et al (2022).
  • ./src/solitude tries to implement Solitude from Lin et al (2022).
  • ./src/data contains all the code to download, pre-process and load graph datasets including Cora, CiteCeer and LastFM.
  • ./src/models contains all the code to build GNN models including GCN, GraphSage and GAT.
• ./scripts is the directory of Python scripts to run experiments.
  • ./scripts/run_blink.sh runs the Blink framework with specified settings.
  • ./scripts/run_baselines.sh runs baseline methods with specified settings.
  • ./scripts/log stores all the log files when running the scripts above.
  • ./scripts/output stores all the results (hyperparameter choices and final accuracy).
• ./doc is the root directory for the paper describing the proposed method.

Running

Inside directory, you can run experiments with python3 run_blink.py {variant name} {dataset} {model_name} --eps {epsilon_list}, like:

python3 run_blink.py hybrid cora gcn --eps 1

Citation

Please cite our paper as follows:

@inproceedings{zhu2023blink,
  author      = {Zhu, Xiaochen and Tan, Vincent Y. F. and Xiao, Xiaokui},
  title       = {Blink: Link Local Differential Privacy in Graph Neural Networks via Bayesian Estimation},
  year        = {2023},
  booktitle   = {Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security},
  location    = {Copenhagen, Denmark},
  series      = {CCS '23}
}

License

The code and documents are licensed under the MIT license.

MIT License

Copyright (c) 2022 Xiaochen Zhu

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