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…e DPQuery to modularize things. The new query takes in an `inner_query` argument. Depending on the behavior of inner query, the query will follow central DP or distributed DP.

(2) Remove the hard-coded L1 clipping and replace with norm bound checking in the inner query. This design allows us to use whatever clipping factory we want outside the DPQuery.

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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

2020-12-21: A new vectorized version of the TF 2 optimizer is available, which can deliver much faster performance. We recommend trying it first, and to fall back to using the original non-vectorized version only if this fails. We are thankful to the authors of this paper for spurring this change.

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 tutorial lm_dpsgd_tutorial.py only)

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 by running pylint --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.

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

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