tensorflow_privacy/research/hyperparameters_2022

Hyperparameter Tuning with Renyi Differential Privacy

Nicolas Papernot and Thomas Steinke

This repository contains the code used to reproduce some of the experiments in our ICLR 2022 paper on hyperparameter tuning with differential privacy.

You can reproduce Figure 7 in the paper by running figure7.py. It loads by default values used to plot the figure contained in the paper, and we also included a dictionary lr_acc.json containing the accuracy of a large number of ML models trained with different learning rates. If you'd like to try our approach to fine-tune your own parameters, you will have to modify the code that interacts with this dictionary (lr_acc in the code from figure7.py).

Citing this work

If you use this repository for academic research, you are highly encouraged (though not required) to cite our paper:

@inproceedings{
papernot2022hyperparameter,
title={Hyperparameter Tuning with Renyi Differential Privacy},
author={Nicolas Papernot and Thomas Steinke},
booktitle={International Conference on Learning Representations},
year={2022},
url={https://openreview.net/forum?id=-70L8lpp9DF}
}