forked from 626_privacy/tensorflow_privacy
30 lines
1.2 KiB
Markdown
30 lines
1.2 KiB
Markdown
# Hyperparameter Tuning with Renyi Differential Privacy
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### Nicolas Papernot and Thomas Steinke
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This repository contains the code used to reproduce some of the experiments in
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our
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[ICLR 2022 paper on hyperparameter tuning with differential privacy](https://openreview.net/forum?id=-70L8lpp9DF).
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You can reproduce Figure 7 in the paper by running `figure7.py`. It loads by
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default values used to plot the figure contained in the paper, and we also
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included a dictionary `lr_acc.json` containing the accuracy of a large number of
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ML models trained with different learning rates. If you'd like to try our
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approach to fine-tune your own parameters, you will have to modify the code that
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interacts with this dictionary (`lr_acc` in the code from `figure7.py`).
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## Citing this work
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If you use this repository for academic research, you are highly encouraged
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(though not required) to cite our paper:
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```
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@inproceedings{
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papernot2022hyperparameter,
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title={Hyperparameter Tuning with Renyi Differential Privacy},
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author={Nicolas Papernot and Thomas Steinke},
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booktitle={International Conference on Learning Representations},
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year={2022},
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url={https://openreview.net/forum?id=-70L8lpp9DF}
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}
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```
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