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
This library uses TensorFlow to define machine learning models. Therefore, installing TensorFlow (>= 1.13) 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:
tensorflow_datasets(for the RNN tutorial
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
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.
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 running
autopep8 -i --indent-size 2 <file>on the files you have edited.
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
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
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.
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.
The content of this repository supersedes the following existing folder in the tensorflow/models repository
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 2019 - Google LLC