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Always checkout Eigen3 and SEAL while building SCI
Bhatu authored 
Locally check them out in extern/ directory so that programs can be linked
against SCI without manually putting them in the networks directory and
modifying the CMake file.

See CompileTFGraph.py for seeing how to build and link programs directly.
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Aramis
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README.md

CrypTFlow: An End-to-end System for Secure TensorFlow Inference contributions welcome HitCount

Reference Papers:

CrypTFlow2: Practical 2-Party Secure Inference
Deevashwer Rathee, Mayank Rathee, Nishant Kumar, Nishanth Chandran, Divya Gupta, Aseem Rastogi, Rahul Sharma
ACM CCS 2020

CrypTFlow: Secure TensorFlow Inference
Nishant Kumar, Mayank Rathee, Nishanth Chandran, Divya Gupta, Aseem Rastogi, Rahul Sharma
IEEE S&P 2020

EzPC: Programmable, Efficient, and Scalable Secure Two-Party Computation for Machine Learning
Nishanth Chandran, Divya Gupta, Aseem Rastogi, Rahul Sharma, Shardul Tripathi
IEEE EuroS&P 2019

Project webpage: https://aka.ms/ezpc

Introduction

This repository has the following components:

  • EzPC: a language for secure machine learning.
  • Athos (part of CrypTFlow): an end-to-end compiler from TensorFlow to a variety of semi-honest MPC protocols. Athos leverages EzPC as a low-level intermediate language.
  • Porthos (part of CrypTFlow): a semi-honest 3 party computation protocol which is geared towards TensorFlow-like applications.
  • Aramis (part of CrypTFlow): a novel technique that uses hardware with integrity guarantees to convert any semi-honest MPC protocol into an MPC protocol that provides malicious security.
  • SCI (part of CrypTFlow2): a semi-honest 2-party computation library for secure inference on deep neural networks.

Each one of the above is independent and usable in their own right and more information can be found in the readme of each of the components. But together these combine to make CrypTFlow a powerful system for end-to-end secure inference of deep neural networks written in TensorFlow.

With these components in place, we are able to run for the first time secure inference on the ImageNet dataset with the pre-trained models of the following deep neural nets: ResNet-50, DenseNet-121 and SqueezeNet for ImageNet.

For setup instructions, please refer to each of the components' readme. We plan to release a docker version of the system as well which will make the system easier to setup.

Wiki

Wiki section of this repository provides coding practices and examples to get started with EzPC.

Issues/Bugs

For bugs and support, please create an issue on the issues page.