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https://github.com/deepmind/jax_privacy
Algorithms for Privacy-Preserving Machine Learning in JAX
https://github.com/deepmind/jax_privacy
Last synced: 27 days ago
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Algorithms for Privacy-Preserving Machine Learning in JAX
- Host: GitHub
- URL: https://github.com/deepmind/jax_privacy
- Owner: google-deepmind
- License: apache-2.0
- Created: 2022-04-21T23:11:04.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-06-18T00:58:06.000Z (7 months ago)
- Last Synced: 2024-12-01T00:39:19.704Z (about 1 month ago)
- Language: Python
- Homepage:
- Size: 197 KB
- Stars: 89
- Watchers: 9
- Forks: 11
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Citation: CITATION.cff
Awesome Lists containing this project
- awesome-differential-privacy - JAX-Privacy - Algorithms for Privacy-Preserving Machine Learning in JAX
README
# JAX-Privacy: Algorithms for Privacy-Preserving Machine Learning in JAX
[**Installation**](#installation)
| [**Reproducing Results**](#reproducing-results)
| [**Citing**](#citing)
This repository contains the JAX implementation of algorithms that we develop
in our research on privacy-preserving machine learning.
This research code is open-sourced with the main objective of
transparency and reproducibility, so (some) rough edges should be expected.
**Note:** to ensure that your installation is compatible with your local
accelerators such as a GPU, we recommend to first follow the corresponding
instructions to install [TensorFlow](https://github.com/tensorflow/tensorflow#install)
and [JAX](https://github.com/google/jax#installation).
### Option 1: Static Installation
This option is preferred for the purpose of re-using functionalities of our
codebase without modifying them.
The package can be installed by running the following command-line:
```
pip install git+https://github.com/google-deepmind/jax_privacy
```
### Option 2: Local Installation (Allowing Edits)
This option is preferred to either build on top of our codebase or to reproduce
our results.
* The first step is to clone the repository:
```
git clone https://github.com/google-deepmind/jax_privacy
```
* Then the code can be installed so that local modifications to the code are
reflected in imports of the package:
```
cd jax_privacy
pip install -e .
```
### Unlocking High-Accuracy Differentially Private Image Classification through Scale
* Instructions: [experiments/image_classification](jax_privacy/experiments/image_classification).
* arXiv link: https://arxiv.org/abs/2204.13650.
* Bibtex reference: [link](https://github.com/google-deepmind/jax_privacy/blob/main/bibtex/de2022unlocking.bib).
### Unlocking Accuracy and Fairness in Differentially Private Image Classification
* Instructions: [experiments/image_classification](jax_privacy/experiments/image_classification).
* arXiv link: https://arxiv.org/abs/2308.10888.
* Bibtex reference: [link](https://github.com/google-deepmind/jax_privacy/blob/main/bibtex/berrada2023unlocking.bib).
## How to Cite This Repository
If you use code from this repository, please cite the following reference:
```
@software{jax-privacy2022github,
author = {Balle, Borja and Berrada, Leonard and De, Soham and Ghalebikesabi, Sahra and Hayes, Jamie and Pappu, Aneesh and Smith, Samuel L and Stanforth, Robert},
title = {{JAX}-{P}rivacy: Algorithms for Privacy-Preserving Machine Learning in JAX},
url = {http://github.com/google-deepmind/jax_privacy},
version = {0.3.0},
year = {2022},
}
```
## Acknowledgements
- [NFNet codebase](
https://github.com/deepmind/deepmind-research/tree/master/nfnets)
- [DeepMind JAX Ecosystem](https://github.com/deepmind/jax/blob/main/deepmind2020jax.txt)
## License
All code is made available under the Apache 2.0 License.
Model parameters are made available under the Creative Commons Attribution 4.0
International (CC BY 4.0) License.
See https://creativecommons.org/licenses/by/4.0/legalcode for more details.
## Disclaimer
This is not an official Google product.