model-updates

Experiments that explore privacy of model updates. Our attacks are shown effective using update information to give the adversary a significant advantage over attacks on standalone models, but also compared to a prior MI attack that takes advantage of model updates in a related machine-unlearning setting. The full paper can be found here: https://arxiv.org/abs/2205.06369

How to Reproduce Paper Figures and Results

All results can be reproduced by running pre-written scripts from the root directory. Make sure to pip install -r requirements.txt before attempting to do so. Using a GPU is highly recommended for CIFAR-10 and IMDb experiments.

FMNIST

1. python script_FMNIST_complete.py Generates results for both single update and multi-update for the FMNIST dataset. Head to the the respective notebooks to plot all figures after script completes.

2. python script_FMNIST_DPSGD.py Generates results for single update training with differential privacy on the FMNIST dataset. Head to the experiments/dpsgd/fashion/noise_experiments notebook to generate DPSGD plots after script completes.

CIFAR-10

1. python script_CIFAR_complete.py Generates results for both single update and multi-update for the CIFAR-10 dataset. Head to the the respective notebooks to plot all figures after script completes.

2. python script_CIFAR_subpop.py Generates results for single update training with drift on the CIFAR-10 dataset. Head to the same directory as above for results.

Purchase-100

1. python script_PURCHASE_complete.py Generates results for both single update and multi-update for the Purchase-100 dataset. Head to the the respective notebooks to plot all figures after script completes.

IMDb

1. python script_IMDB_complete.py Generates results for both single update and multi-update for the IMDb dataset. Head to the the respective notebooks to plot all figures after script completes.

Citation

If you publish work that uses code from this repository, please cite as follows:

@article{jagielski2022combine,
  title={How to Combine Membership-Inference Attacks on Multiple Updated Models},
  author={Jagielski, Matthew and Wu, Stanley and Oprea, Alina and Ullman, Jonathan and Geambasu, Roxana},
  journal={arXiv preprint arXiv:2205.06369},
  year={2022}
}