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 to define machine
learning models. Therefore, installing TensorFlow (>= 1.14) is a pre-requisite.
You can find instructions here. 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) -
tensorflow_datasets(for the RNN tutoriallm_dpsgd_tutorial.pyonly)
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 spacescoding style (the same as the one used by TensorFlow) in
your pull requests. In most cases this can be done by runningautopep8 -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
by runningpylint --rcfile=/path/to/the/tf/rcfile <edited file.py>. -
When making your first pull request, you
sign the Google CLA -
We do not accept pull requests that add git submodules because of
the problems that arise when maintaining git submodules
Tutorials directory
To help you get started with the functionalities provided by this library, we
provide a detailed walkthrough here 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.
Remarks
The content of this repository supersedes the following existing folder in the
tensorflow/models repository
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