w-yk / privacy

Library for training machine learning models with privacy for training data

Intro

This repository is forked from TensorFlow Privacy . Added adaptive clip and gradient compression features.

Usage

The three file tutorials/adult_compress.py , tutorials/mnist_compress.py , tutorials/cifar_compresss.py , contains the code for gradient compression. To use adaptive clip method , change the default imported optimizer into the one in dp_optimizer_keras_vectorized_adaclip.

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.

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