/Private-Set

Official implementation of "Private Set Generation with Discriminative Information" (NeurIPS 2022)

Primary LanguagePythonMIT LicenseMIT

Private-Set

LICENSE Python PyTorch

image This repository contains the implementation for "Private Set Generation with Discriminative Information (NeurIPS 2022)".

Contact: Dingfan Chen (dingfan.chen@cispa.de)

Requirements

  • This implementation is based on PyTorch (tested for version 1.7.1).
  • Please refer to requirements.txt for the other required packages and version.
  • The differentially private training part is based on Opacus with version 0.15.0.
    Note that due to significant refactoring, later versions of Opacus (>=1.0.0) is not compatible with our implementation at the moment.

Running Experiments

Classification (without prior)

python main.py \
--dataset "Dataset name" \
--exp_name "Experiment name" (name of the output directory) \
--spc "Number of samples per-class" \
--enable_privacy \
--target_epsilon "The target privacy level (default=10)"
  • See main.py or run python main.py --help for a detailed descriptions of all supported arguments.
  • Privacy cost computation is done by: automatically computing the required noise_multiplier given the desired target_epsilon.
  • Dataset: 'MNIST','FashionMNIST', 'CIFAR10' has been tested.
  • Number of samples per-class: spc in [1, 10, 20, 50] has been tested.

Classification (with DCGAN network as image prior)

python main_prior.py \
--dataset "Dataset name" \
--exp_name "Experiment name" (name of the output directory) \
--spc "Number of samples per-class" \
--enable_privacy \
--target_epsilon "The target privacy level (default=10)"
  • See main_prior.py or run python main_prior.py --help for a detailed descriptions of all supported arguments.
  • Privacy cost computation is done by: automatically computing the required noise_multiplier given the desired target_epsilon.
  • Dataset: 'MNIST','FashionMNIST' has been tested.
  • Number of samples per-class: spc in [1, 10, 20] has been tested.

Continual (Class-incremental) learning

cd continual_learning
  • Dataset: 'MNIST','FashionMNIST' are supported.
  • Run Ours 
    python main_ours.py \
--dataset "Dataset name" \
--exp_name "Experiment name" (name of the output directory) \
--enable_privacy \
--target_epsilon "The target privacy level (default=10)"
  • Run DP-SGD baseline 
    python main_dpsgd.py \
--dataset "Dataset name" \
--exp_name "Experiment name" (name of the output directory) \
--enable_privacy \
--target_epsilon "The target privacy level (default=10)"
  • Run DP-Merf baseline 
    python main_dpmerf.py \
--dataset "Dataset name" \
--exp_name "Experiment name" (name of the output directory) \
--enable_privacy \
--target_epsilon "The target privacy level (default=10)"

Citation

@inproceedings{chen2022privateset,
  title={Private Set Generation with Discriminative Information},
  author={Chen, Dingfan and Kerkouche, Raouf and Fritz, Mario},
  booktitle={Neural Information Processing Systems (NeurIPS)},
  year={2022}
}

Acknowledgements

Our implementation uses the source code from the following repositories: