Generalized Linear Bandits with Local Differential Privacy

This repository is the official implementation of Generalized Linear Bandits with Local Differential Privacy by Yuxuan Han, Zhipeng Liang, Yang Wang, and Jiheng Zhang.

Requirements:

Requires python 3, numpy, matplotlib, etc. Please use the following command to install the dependencies:

pip install -r requirements.txt

Datasets:

We evaluate our algorithms in a real data CRPM-12-001: On-Line Auto Lending dataset, provided by the Center for Pricing and Revenue Management at Columbia University. To obtain a data set, please go to the website and follow their instructions

Citation:

If you wish to use our repository in your work, please cite our paper:

BibTex:

@article{han2021generalized,
  title={Generalized Linear Bandits with Local Differential Privacy},
  author={Han, Yuxuan and Liang, Zhipeng and Wang, Yang and Zhang, Jiheng},
  journal={Advances in Neural Information Processing Systems},
  year={2021}
}

Any question about the scripts can be directed to the authors via email.

License

This project is licensed under the MIT License - see the LICENSE file for details

Generating the figures in the paper:

'./Scheme1/' folder is for step size $1/t$ version of LDP-SGD, which is originally designed for low dimensional setting

'./Scheme2/' folder is for step size $1/\sqrt{t}$ version of LDP-SGD, which is use for producing figures in Appendix F.

For generating the figures in the paper please execute the following codes:

• Figure 2
  • Single-param Experiments:

  cd Scheme1
  nohup python3 instances/singleparam.py --eps 1  --dest "/results/single-param-eps=1/" > singleparam.out 2>&1 &
  nohup python3 instances/singleparam.py --eps 0.5  --dest "/results/single-param-eps=0.5/" > singleparam.out 2>&1 &
  

  • Multi-param Experiments in Figure 2:

  cd Scheme1
  nohup python3 instances/multiparam.py --eps 1  --dest "/results/multi-param-eps=1/" > multiparam.out 2>&1 &
  nohup python3 instances/multiparam.py --eps 0.5  --dest "/results/multi-param-eps=0.5/" > multiparam.out 2>&1 &
  

• Figure 3
  • Single-param Experiments:

  cd Scheme2
  nohup python3 instances/singleparam.py --dimension 20 --n_actions 20 --eps 0.5 --dest '/results/single-param-d=20-k=20-eps=0.5/' > singleparam.out 2>&1 &
  nohup python3 instances/singleparam.py --dimension 20 --n_actions 20 --eps 1 --dest '/results/single-param-d=20-k=20-eps=1/' > singleparam.out 2>&1 &
  

  • Multi-param Experiments:

  cd Scheme2
  nohup python3 instances/multiparam.py --dimension 10 --n_actions 10 --eps 0.5 --dest '/results/multiparam-d=10-k=10-eps=0.5/' > multiparam.out 2>&1 &
  nohup python3 instances/multiparam.py --dimension 10 --n_actions 10 --eps 1 --dest '/results/multiparam-d=10-k=10-eps=1/' > multiparam.out 2>&1 &
  

• Real-data Experiments Figure 4:

cd Scheme1
nohup python3 instances/crpm.py --eps 1  --dest "/results/crpm-eps=1/" > crpm.out 2>&1 &
nohup python3 instances/crpm.py --eps 0.5  --dest "/results/crpm-eps=0.5/" > crpm.out 2>&1 &