tensorflow_privacy/README.md

# 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.

## Setting up TensorFlow Privacy

### Dependencies

This library uses [TensorFlow](https://www.tensorflow.org/) to define machine
learning models. Therefore, installing TensorFlow 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).

In addition to TensorFlow and its dependencies, other prerequisites are:
  * `scipy` >= 0.17
  * `mpmath` (for testing)

### Installing TensorFlow Privacy

First, 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.

*   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, the
`tutorials/` folder comes with scripts demonstrating how to use the library
features.

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.

## 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 2018 - Google LLC