Skip to content
Layer on top of TensorFlow for doing machine learning on encrypted data
Branch: master
Clone or download
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
.circleci
bin rename register to registry in convert/register.py Apr 17, 2019
docs
examples
models fix mnist example with .pb file (#247) Oct 17, 2018
operations
tests
tf_encrypted
tools/gcp
.dockerignore dont copy build artifacts when building docker container (#305) Nov 23, 2018
.gitignore
CHANGELOG.md 0.5.1 release (#432) Apr 16, 2019
Dockerfile
LICENSE Dropout Labs Inc., Contributions (#91) Sep 6, 2018
Makefile rm unnecessary CURRENT_DIR in makefile, lint Apr 17, 2019
NOTICE
README.md Add a file requiring version bump to RELEASING (#431) Apr 16, 2019
docker-compose.yml Naming (#257) Oct 23, 2018
meta.yaml
requirements.txt how-to guide for adding to converter, autogen reserved scopes from yaml Apr 17, 2019
setup.py
tox.ini Bump flake8 and fix CI dependency caching (#291) Nov 19, 2018

README.md

TF Encrypted

Status License PyPI CircleCI Badge Documentation

TF Encrypted is a Python library built on top of TensorFlow for researchers and practitioners to experiment with privacy-preserving machine learning. It provides an interface similar to that of TensorFlow, and aims at making the technology readily available without first becoming an expert in machine learning, cryptography, distributed systems, and high performance computing.

In particular, the library focuses on:

  • Usability: The API and its underlying design philosophy make it easy to get started, use, and integrate privacy-preserving technology into pre-existing machine learning processes.
  • Extensibility: The architecture supports and encourages experimentation and benchmarking of new cryptographic protocols and machine learning algorithms.
  • Performance: Optimizing for tensor-based applications and relying on TensorFlow's backend means runtime performance comparable to that of specialized stand-alone frameworks.
  • Community: With a primary goal of pushing the technology forward the project encourages collaboration and open source over proprietary and closed solutions.
  • Security: Cryptographic protocols are evaluated against strong notions of security and known limitations are highlighted.

See below for more background material, explore the examples, or visit the documentation to learn more about how to use the library.

The project has benefitted enormously from the efforts of several contributors following its original implementation, most notably Dropout Labs and members of the OpenMined community. See below for further details.

Installation

TF Encrypted is available as a package on PyPI supporting Python 3.5+ and TensorFlow 1.12.0+ which can be installed using:

pip3 install tf-encrypted

Alternatively, installing from source can be done using:

git clone https://github.com/mortendahl/tf-encrypted.git
cd tf-encrypted
pip3 install -r requirements.txt
pip3 install -e .

This latter is useful on platforms for which the pip package has not yet been compiled but is also needed for development. Note that this will get you a working basic installation, yet a few more steps are required to match the performance and security of the version shipped in the pip package, see the installation instructions.

Custom build of TensorFlow For 1.12.0

TF Encrypted officially supports TensorFlow 1.13.1 but if you have a need to run on 1.12.0 and want to take advantage of the int64 tensor speed improvements you'll have to make use of a custom build.

Such builds are available for macOS and Linux as a temporary solution until the next official release of TensorFlow is out (version 1.13), but no guarantees are made about them and they should be treated as pre-alpha. See more in the installation instructions.

Usage

The following is an example of simple matmul on encrypted data using TF Encrypted:

import tensorflow as tf
import tf_encrypted as tfe

def provide_input():
    # normal TensorFlow operations can be run locally
    # as part of defining a private input, in this
    # case on the machine of the input provider
    return tf.ones(shape=(5, 10))

# define inputs
w = tfe.define_private_variable(tf.ones(shape=(10,10)))
x = tfe.define_private_input('input-provider', provide_input)

# define computation
y = tfe.matmul(x, w)

with tfe.Session() as sess:
    # initialize variables
    sess.run(tfe.global_variables_initializer())
    # reveal result
    result = sess.run(y.reveal())

For more information, check out the documentation or the examples.

Background & Further Reading

The following texts provide further in-depth presentations of the project:

Project Status

TF Encrypted is experimental software not currently intended for use in production environments. The focus is on building the underlying primitives and techniques, with some practical security issues postponed for a later stage. However, care is taken to ensure that none of these represent fundamental issues that cannot be fixed as needed.

Known limitations

  • Elements of TensorFlow's networking subsystem does not appear to be sufficiently hardened against malicious users. Proxies or other means of access filtering may be sufficient to mitigate this.

Contributing

Don't hesitate to send a pull request, open an issue, or ask for help! Check out our contribution guide for more information!

Several individuals have already had an impact on the development of this library (in alphabetical order):

and several companies have invested significant resources:

  • Dropout Labs continues to sponsor a large amount of both research and engineering
  • OpenMined was the breeding ground for the initial idea and continues to support discussions and guidance

License

Licensed under Apache License, Version 2.0 (see LICENSE or http://www.apache.org/licenses/LICENSE-2.0). Copyright as specified in NOTICE.

You can’t perform that action at this time.