Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/facebookresearch/CrypTen

A framework for Privacy Preserving Machine Learning
https://github.com/facebookresearch/CrypTen

Last synced: 4 days ago
JSON representation

A framework for Privacy Preserving Machine Learning

Awesome Lists containing this project

README 

        
CrypTen logo



[![Support Ukraine](https://img.shields.io/badge/Support-Ukraine-FFD500?style=flat&labelColor=005BBB)](https://opensource.fb.com/support-ukraine) [![GitHub license](https://img.shields.io/badge/license-MIT-blue.svg)](https://github.com/facebookresearch/CrypTen/blob/master/LICENSE) [![CircleCI](https://circleci.com/gh/facebookresearch/CrypTen.svg?style=shield)](https://circleci.com/gh/facebookresearch/CrypTen/tree/master) [![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg)](https://github.com/facebookresearch/CrypTen/blob/master/CONTRIBUTING.md)



--------------------------------------------------------------------------------



CrypTen is a framework for Privacy Preserving Machine Learning built on PyTorch.

Its goal is to make secure computing techniques accessible to Machine Learning practitioners.

It currently implements [Secure Multiparty Computation](https://en.wikipedia.org/wiki/Secure_multi-party_computation)

as its secure computing backend and offers three main benefits to ML researchers:



1. It is machine learning first. The framework presents the protocols via a `CrypTensor`

   object that looks and feels exactly like a PyTorch `Tensor`. This allows the user to use

   automatic differentiation and neural network modules akin to those in PyTorch.



2. CrypTen is library-based. It implements a tensor library just as PyTorch does.

   This makes it easier for practitioners to debug, experiment on, and explore ML models.



3. The framework is built with real-world challenges in mind. CrypTen does not scale back or

   oversimplify the implementation of the secure protocols.



Here is a bit of CrypTen code that encrypts and decrypts tensors and adds them



```python

import torch

import crypten



crypten.init()



x = torch.tensor([1.0, 2.0, 3.0])

x_enc = crypten.cryptensor(x) # encrypt



x_dec = x_enc.get_plain_text() # decrypt



y_enc = crypten.cryptensor([2.0, 3.0, 4.0])

sum_xy = x_enc + y_enc # add encrypted tensors

sum_xy_dec = sum_xy.get_plain_text() # decrypt sum

```



It is currently not production ready and its main use is as a research framework.



## Installing CrypTen



CrypTen currently runs on Linux and Mac with Python 3.7.

We also support computation on GPUs.

Windows **is not** supported.



_For Linux or Mac_

```bash

pip install crypten

```



If you want to run the examples in the `examples` directory, you should also do the following

```bash

pip install -r requirements.examples.txt

```



## Examples

To run the examples in the `examples` directory, you additionally need to clone the repo and



```bash

pip install -r requirements.examples.txt

```



We provide examples covering a range of models in the `examples` directory



1. The linear SVM example, `mpc_linear_svm`, generates random data and trains a

  SVM classifier on encrypted data.

2. The LeNet example, `mpc_cifar`, trains an adaptation of LeNet on CIFAR in

  cleartext and encrypts the model and data for inference.

3. The TFE benchmark example, `tfe_benchmarks`, trains three different network

  architectures on MNIST in cleartext, and encrypts the trained model and data

  for inference.

4. The bandits example, `bandits`, trains a contextual bandits model on

  encrypted data (MNIST).

5. The imagenet example, `mpc_imagenet`, performs inference on pretrained

  models from `torchvision`.



For examples that train in cleartext, we also provide pre-trained models in

cleartext in the `model` subdirectory of each example subdirectory.



You can check all example specific command line options by doing the following;

shown here for `tfe_benchmarks`:



```bash

python examples/tfe_benchmarks/launcher.py --help

```



## How CrypTen works



We have a set of tutorials in the `tutorials` directory to show how

CrypTen works. These are presented as Jupyter notebooks so please install

the following in your conda environment



```bash

conda install ipython jupyter

pip install -r requirements.examples.txt

```



1. `Introduction.ipynb` - an introduction to Secure Multiparty Compute; CrypTen's

   underlying secure computing protocol; use cases we are trying to solve and the

   threat model we assume.

2. `Tutorial_1_Basics_of_CrypTen_Tensors.ipynb` - introduces `CrypTensor`, CrypTen's

   encrypted tensor object, and shows how to use it to do various operations on

   this object.

3. `Tutorial_2_Inside_CrypTensors.ipynb` – delves deeper into `CrypTensor` to show

   the inner workings; specifically how `CrypTensor` uses `MPCTensor` for its

   backend and the two different kind of _sharings_, arithmetic and binary, are

   used for two different kind of functions. It also shows CrypTen's

   [MPI](https://en.wikipedia.org/wiki/Message_Passing_Interface)-inspired

   programming model.

4. `Tutorial_3_Introduction_to_Access_Control.ipynb` - shows how to train a linear

   model using CrypTen and shows various scenarios of data labeling, feature

   aggregation, dataset augmentation and model hiding where this is applicable.

5. `Tutorial_4_Classification_with_Encrypted_Neural_Networks.ipynb` – shows how

   CrypTen can load a pre-trained PyTorch model, encrypt it and then do inference

   on encrypted data.

6. `Tutorial_5_Under_the_hood_of_Encrypted_Networks.ipynb` - examines how CrypTen

   loads PyTorch models, how they are encrypted and how data moves through a multilayer

   network.

7. `Tutorial_6_CrypTen_on_AWS_instances.ipynb` - shows how to use `scrips/aws_launcher.py`

   to launch our examples on AWS. It can also work with your code written in CrypTen.

8. `Tutorial_7_Training_an_Encrypted_Neural_Network.ipynb` - introduces the

   automatic differentiation functionality of `CrypTensor`. This functionality

   makes it easy to train neural networks in CrypTen.



## Documentation and citing

CrypTen is documented [here](https://crypten.readthedocs.io/en/latest/).



The protocols and design protocols implemented in CrypTen are described in [this paper](https://arxiv.org/pdf/2109.00984.pdf). If you want to cite CrypTen in your papers (much appreciated!), you can cite it as follows:

```

@inproceedings{crypten2020,

  author={B. Knott and S. Venkataraman and A.Y. Hannun and S. Sengupta and M. Ibrahim and L.J.P. van der Maaten},

  title={CrypTen: Secure Multi-Party Computation Meets Machine Learning},

  booktitle={arXiv 2109.00984},

  year={2021},

}

```



## Join the CrypTen community

Please contact [us](mailto:[email protected]) to join the CrypTen community on [Slack](https://cryptensor.slack.com)



See the [CONTRIBUTING](CONTRIBUTING.md) file for how to help out.



## License

CrypTen is MIT licensed, as found in the LICENSE file.