# 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 guarantees provided using analysis
tools included in TF Privacy.

In addition, the `tutorials/` folder comes with scripts demonstrating how to use
the library features. The list of tutorials is described in the README included
in the tutorials directory.

NOTE: the tutorials are maintained carefully. However, they are not considered
part of the API and they can change at any time without warning. You should not
write 3rd party code that imports the tutorials and expect that the interface
will not break.

## Research directory

This folder contains code to reproduce results from research papers related to
privacy in machine learning. It is not maintained as carefully as the tutorials
directory, but rather intended as a convenient archive.

## TensorFlow 2.x

TensorFlow Privacy now works with TensorFlow 2! You can use the new Keras-based
estimators found in
`privacy/tensorflow_privacy/privacy/optimizers/dp_optimizer_keras.py`.

For this to work with `tf.keras.Model` and `tf.estimator.Estimator`, however,
you need to install TensorFlow 2.4 or later.

## Remarks

The content of this repository supersedes the following existing folder in the
tensorflow/models
[repository](https://github.com/tensorflow/models/tree/master/research/differential_privacy)

## Contacts

If you have any questions that cannot be addressed by raising an issue, feel
free to contact:

*   Galen Andrew (@galenmandrew)
*   Steve Chien (@schien1729)
*   Nicolas Papernot (@npapernot)

## Copyright

Copyright 2019 - Google LLC
