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https://github.com/tensorflow/privacy

Library for training machine learning models with privacy for training data
https://github.com/tensorflow/privacy

machine-learning privacy

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Library for training machine learning models with privacy for training data

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README 

        
# 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 ` 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 `.



*   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