Homomorphically Encrypted Deep Learning Library

The goal of this library is to give the user the ability to efficiently train Deep Learning models in a homomorphically encrypted state, without needing to be an expert in either. Furthermore, by understanding the characteristics of both Deep Learning and Homomorphic Encryption, we hope to find a very performant combinations of the two. Also, check the main demonstration from the Sonar project.

• Local setup
• For Contributors
• Relevant Literature
• License

• Make sure Python 3.5+ in installed on your machine by checking python3 --version
• Set up a virtual environment for the Python libraries (optional, recommended)
• Install our OpenMined Unity app by following the guidelines here

The Python dependencies are listed in requirements.txt and can be installed through

pip3 install -r requirements.txt

If you simply want to to use PySyft, it is enough to install the library with:

python3 setup.py install

note: If you have anaconda installed, you can just run bash install_for_anaconda.sh.

If you are interested in contributing to Syft, first check out our Contributor Quickstart Guide and then sign into our Slack Team channel #team_pysyft to let us know which projects sound interesting to you! (or propose your own!).

As both Homomorphic Encryption and Deep Learning are still somewhat sparsely known, below is a curated list of relevant reading materials to bring you up to speed with the major concepts and themes of these exciting fields.

• How to build a fully encrypted AI model (trained on unencrypted data)
• Simple secure protocol for federated machine learning (using a python-paillier library)
• Prototype for using encrypted AI to preserve user privacy (in python)
• Manual for Using Homomorphic Encryption for Bioinformatics (paper)

• A Comparison of the Homomorphic Encryption Schemes
• Homomorphic Encryption API Software Library
• Faster Homomorphic Function Evaluation using Non-Integral Base Encoding

• Encrypted accelerated least squares regression
• PSML 2017 - Abstracts
• Encrypted statistical machine learning: new privacy preserving methods
• A review of homomorphic encryption and software tools for encrypted statistical machine learning
• Privacy-Preserving Distributed Linear Regression on High-Dimensional Data
• Secure Computation With Fixed-Point Numbers
• Scalable and secure logistic regression via homomorphic encryption
• ML Confidential: Machine Learning on Encrypted Data
• CryptoNets: Applying Neural Networks to Encrypted Data with High Throughput and Accuracy
• Privacy-Preserving Visual Learning Using Doubly Permuted Homomorphic Encryption

• HomomorphicEncryption - An R package for fully homomorphic encryption
• A Secure Multiparty Computation (MPC) protocol for computing linear regression on vertically distributed datasets
• Dask Tutorial
• Charm-crypto

• Private Deep Learning with MPC - A Simple Tutorial from Scratch
• Secret Sharing, Part 1 - Distributing Trust and Work
• Secret Sharing, Part 2 - Efficient Sharing with the Fast Fourier Transform
• Distributed machine learning and partially homomorphic encryption (Part 1)
• Distributed machine learning and partially homomorphic encryption (Part 2)
• Tutorial: How to verify crowdsourced training data using a Known Answer Review Policy

Apache-2.0 by OpenMined contributors