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Library for training machine learning models with privacy for training data
| Date | Stars |
|---|---|
| 2026-07-24 | 2019 |
| 2026-07-25 | 2019 |
| 2026-07-28 | 2019 |
| 2026-07-30 | 2019 |
| 2026-07-31 | 2021 |
| 2026-08-06 | 2021 |
Today
— stars today
This week
+2 stars this week
This month
— stars this month
Momentum
2.0
growth rate 0.10%/day
# TensorFlow Privacy
This repository contains the source code for TensorFlow Privacy, a Python
library that includes implementations of TensorFlow optimizers for training
machine learning models with differential privacy. The library comes with
tutorials and analysis tools for computing the privacy guarantees provided.
The TensorFlow Privacy library is under continual development, always welcoming
contributions. In particular, we always welcome help towards resolving the
issues currently open.
## Latest Updates
2024-02-14: As of version 0.9.0, the TensorFlow Privacy github repository will
be published as two separate PyPI packages. The first will inherit the name
tensorflow-privacy and contain the parts related to training of DP models. The
second, tensorflow-empirical-privacy, will contain the parts related to testing
for empirical privacy.
2023-02-21: A new implementation of efficient per-example gradient clipping is
now available for
[DP keras models](https://github.com/tensorflow/privacy/tree/master/tensorflow_privacy/privacy/keras_models)
consisting only of Dense and Embedding layers. The models use the fast gradient
calculation results of [this paper](https://arxiv.org/abs/1510.01799). The
implementation should allow for doing DP training without any meaningful memory
or runtime overhead. It also removes the need for tuning the number of
microbatches as it clips the gradient with respect to each example.
## Setting up TensorFlow Privacy
### Dependencies
This library uses [TensorFlow](https://www.tensorflow.org/) to define machine
learning models. Therefore, installing TensorFlow (>= 1.14) is a pre-requisite.
You can find instructions [here](https://www.tensorflow.org/install/). For
better performance, it is also recommended to install TensorFlow with GPU
support (detailed instructions on how to do this are available in the TensorFlow
installation documentation).
### Installing TensorFlow Privacy
If you only want to use TensorFlow Privacy as a library, you can simply execute
`pip install tensorflow-privacy`
Otherwise, you can clone this GitHub repository into a directory of your choice:
```
git clone https://github.com/tensorflow/privacy
```
You can then install the local package in "editable" mode in order to add it to
your `PYTHONPATH`:
```
cd privacy
pip install -e .
```
If you'd like to make contributions, we recommend first forking the repository
and then cloning your fork rather than cloning this repository directly.
## Contributing
Contributions are welcomed! Bug fixes and new features can be initiated through
GitHub pull requests. To speed the code review process, we ask that:
* When making code contributions to TensorFlow Privacy, you follow the `PEP8
with two spaces` coding style (the same as the one used by TensorFlow) in
your pull requests. In most cases this can be done by running `autopep8 -i
--indent-size 2 <file>` on the files you have edited.
* You should also check your code with pylint and TensorFlow's pylint
[configuration file](https://raw.githubusercontent.com/tensorflow/tensorflow/master/tensorflow/tools/ci_build/pylintrc)
by running `pylint --rcfile=/path/to/the/tf/rcfile <edited file.py>`.
* When making your first pull request, you
[sign the Google CLA](https://cla.developers.google.com/clas)
* We do not accept pull requests that add git submodules because of
[the problems that arise when maintaining git submodules](https://medium.com/@porteneuve/mastering-git-submodules-34c65e940407)
## Tutorials directory
To help you get started with the functionalities provided by this library, we
provide a detailed walkthrough [here](tutorials/walkthrough/README.md) that will
teach you how to wrap existing optimizers (e.g., SGD, Adam, ...) into their
differentially private counterparts using TensorFlow (TF) Privacy. You will also
learn how to tune the parameters introduced by differentially private
optimization and how to measure the privacy guaranteeExcerpt of 5,506 characters
Read on GitHub
TensorFlower Gardener
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Mark Daoust
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Yilei · United States
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:efdd0bb34511d075, topic:privacy, readme:differential privacy