https://github.com/NervanaSystems/he-transformer
nGraph-HE: Deep learning with Homomorphic Encryption (HE) through Intel nGraph
https://github.com/NervanaSystems/he-transformer
compiler deep-learning homomorphic-encryption ngraph privacy-preserving seal tensorflow
Last synced: about 1 month ago
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nGraph-HE: Deep learning with Homomorphic Encryption (HE) through Intel nGraph
- Host: GitHub
- URL: https://github.com/NervanaSystems/he-transformer
- Owner: NervanaSystems
- License: apache-2.0
- Archived: true
- Created: 2018-04-09T18:14:49.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2023-01-03T22:54:12.000Z (over 2 years ago)
- Last Synced: 2024-07-30T19:16:27.578Z (9 months ago)
- Topics: compiler, deep-learning, homomorphic-encryption, ngraph, privacy-preserving, seal, tensorflow
- Language: C++
- Homepage:
- Size: 92.1 MB
- Stars: 222
- Watchers: 32
- Forks: 39
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# DISCONTINUATION OF PROJECT #
This project will no longer be maintained by Intel.
Intel has ceased development and contributions including, but not limited to, maintenance, bug fixes, new releases, or updates, to this project.
Intel no longer accepts patches to this project.
# HE Transformer for nGraph
# This project has moved to https://github.com/IntelAI/he-transformer
The **Intel® HE transformer for nGraph™** is a Homomorphic Encryption (HE) backend to the [**Intel® nGraph Compiler**](https://github.com/NervanaSystems/ngraph), Intel's graph compiler for Artificial Neural Networks.
Homomorphic encryption is a form of encryption that allows computation on encrypted data, and is an attractive remedy to increasing concerns about data privacy in the field of machine learning. For more information, see our [original paper](https://arxiv.org/pdf/1810.10121.pdf). Our [updated paper](https://arxiv.org/pdf/1908.04172.pdf) showcases many of the recent advances in he-transformer.
This project is meant as a proof-of-concept to demonstrate the feasibility of HE on local machines. The goal is to measure performance of various HE schemes for deep learning. This is **not** intended to be a production-ready product, but rather a research tool.
Currently, we support the [CKKS](https://eprint.iacr.org/2018/931.pdf) encryption scheme, implemented by the [Simple Encrypted Arithmetic Library (SEAL)](https://github.com/Microsoft/SEAL) from Microsoft Research.
Additionally, we integrate with the [**Intel® nGraph™ Compiler and runtime engine for TensorFlow**](https://github.com/tensorflow/ngraph-bridge) to allow users to run inference on trained neural networks through Tensorflow.
## Examples
The [examples](https://github.com/NervanaSystems/he-transformer/tree/master/examples) folder contains a deep learning example which depends on the [**Intel® nGraph™ Compiler and runtime engine for TensorFlow**](https://github.com/tensorflow/ngraph-bridge).
## Building HE Transformer
### Dependencies
- Operating system: Ubuntu 16.04, Ubuntu 18.04.
- CMake >= 3.12
- Compiler: g++ version >= 6.0, clang >= 5.0
- OpenMP is strongly suggested, though not strictly necessary. You may experience slow runtimes without OpenMP
- python3 and pip3
- virtualenv v16.1.0
- bazel v0.25.2
For a full list of dependencies, see the [docker containers](https://github.com/NervanaSystems/he-transformer/tree/master/contrib/docker), which build he-transformer on a reference OS.
#### The following dependencies are built automatically
- [nGraph](https://github.com/NervanaSystems/ngraph) - v0.27.0-rc.0
- [nGraph-tf](https://github.com/tensorflow/ngraph-bridge) - v0.21.0-rc0
- [SEAL](https://github.com/Microsoft/SEAL) - v3.4.2
- [TensorFlow](https://github.com/tensorflow/tensorflow) - v1.14.0
- [Boost](https://github.com/boostorg) v1.69
- [Google protobuf](https://github.com/protocolbuffers/protobuf) v3.10.1
### To install bazel
```bash
wget https://github.com/bazelbuild/bazel/releases/download/0.25.2/bazel-0.25.2-installer-linux-x86_64.sh
bash bazel-0.25.2-installer-linux-x86_64.sh --user
```
Add and source the bin path to your `~/.bashrc` file to call bazel
```bash
export PATH=$PATH:~/bin
source ~/.bashrc
```
### 1. Build HE-Transformer
Before building, make sure you deactivate any active virtual environments (i.e. run `deactivate`)
```bash
git clone https://github.com/NervanaSystems/he-transformer.git
cd he-transformer
export HE_TRANSFORMER=$(pwd)
mkdir build
cd $HE_TRANSFORMER/build
cmake .. -DCMAKE_CXX_COMPILER=clang++-6.0
make install
source external/venv-tf-py3/bin/activate
```
Note, you may need sudo permissions to install he_seal_backend to the default location. To set a custom installation prefix, add the `-DCMAKE_INSTALL_PREFIX=~/my_install_prefix` flag to the cmake command.
#### 1a. To build documentation
First install doxygen, i.e.
```bash
sudo apt-get install doxygen
```
Then add the following CMake flag
```bash
cmake .. -DNGRAPH_HE_DOC_BUILD_ENABLE=ON
```
and call
```bash
make docs
```
to create doxygen documentation in `$HE_TRANSFORMER/build/doc/doxygen`.
#### 1b. Python bindings for client
To build a client-server model with python bindings (recommended for running neural networks through TensorFlow):
```bash
cd $HE_TRANSFORMER/build
source external/venv-tf-py3/bin/activate
make install python_client
```
This will create `python/dist/pyhe_client-*.whl`. Install it using
```bash
pip install python/dist/pyhe_client-*.whl
```
To check the installation worked correctly, run
```bash
python3 -c "import pyhe_client"
```
This should run without errors.
### 2. Run C++ unit-tests
```bash
cd $HE_TRANSFORMER/build
# To run single HE_SEAL unit-test
./test/unit-test --gtest_filter="HE_SEAL.add_2_3_cipher_plain_real_unpacked_unpacked"
# To run all C++ unit-tests
./test/unit-test
```
### 3. Run python examples
See [examples/README.md](https://github.com/NervanaSystems/he-transformer/tree/master/examples/README.md) for examples of running he-transformer for deep learning inference on encrypted data.
## Code formatting
Please run `maint/apply-code-format.sh` before submitting a pull request.