This repository contains code that can be used to reproduce the experimental results presented in the paper:
Awni Hannun, Chuan Guo and Laurens van der Maaten. Measuring Data Leakage in Machine-Learning Models with Fisher Information. arXiv 2102.11673, 2021.
The code requires Python 3.7+, PyTorch 1.7.1+, and torchvision 0.8.2+.
Create an Anaconda environment and install the dependencies:
conda create --name fil
conda activate fil
conda install -c pytorch pytorch torchvision
pip install gitpython numpy
The script fisher_information.py computes the per-example FIL for the given
dataset and model. An example run is:
python fisher_information.py \
--dataset mnist \
--model least_squares
To see usage options for the script run:
python fisher_information.py --help
Other scripts in the repository are:
reweighted.py: Run the iteratively reweighted Fisher information loss (IRFIL) algorithm.model_inversion.py: Attribute inversion experiments for a non-private model.private_model_inversion.py: Attribute inversion experiments for a private model.test_jacobians.py: Unit tests.
To run the full set of experiments in the accompanying paper:
cd scripts/ && ./run_experiments.sh
If you use the code in this repository, please cite the following paper:
@inproceedings{hannun2021fil,
title={Measuring Data Leakage in Machine-Learning Models with Fisher
Information},
author={Hannun, Awni and Guo, Chuan and van der Maaten, Laurens},
booktitle={Conference on Uncertainty in Artificial Intelligence},
year={2021}
}
This code is released under a CC-BY-NC 4.0 license. Please see the LICENSE file for more information.
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