Awesome

Private-Set

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: