FSS-DT

Introduction

The code of FSS-DT is on application/Lightweiht_disicion_tree.

This project is a secure multi-party computation library that designs and implements privacy-preserving computation protocols based on arithmetic secret sharing and function secret sharing. It also utilizes these protocols to implement the application of privacy-preserving machine learning.

Installation tutorial

This project requires PyTorch>=1.8.0, and it is recommended to use PyTorch==1.8.0. Other dependencies are listed in the ./requirements.txt file. You can install the project dependencies by executing the following command:

pip install -r requirements.txt

Getting start

All test codes needs to be run in the project root directory, as shown in the following example:

# Open two terminals and input the following code in each terminal:
python debug/application/neural_network/2pc/neural_network_server.py
python debug/application/neural_network/2pc/neural_network_client.py

If you cannot start using the above command, try adding the following code to the beginning of the corresponding test file:

import sys
sys.path.append('/path/to/the/directory/mpctensorlib')
print(sys.path)

For instructions on how to use the library for a privacy application, please refer to the tutorials in the pack 'tutorials', which are presented as a Jupyter notebook, so please install the following in your conda environment:

conda install ipython jupyter

1.Tutorial_0_Before_Starting.ipynb - Before starting the tutorial, this notebook provides an introduction to the configuration information and auxiliary parameters required for computations in the library.
2.Tutorial_1_Ring_Tensor.ipynb - This tutorial introduces the basic data type RingTensor in the library. It demonstrates how to perform basic operations using RingTensor.
3.Tutorial_2_Arithmetic_Secret_Sharing.ipynb - This tutorial explains the basic data type, ArithmeticSharedRingTensor, used for secure multi-party computation in the library. It shows how to perform basic operations using ArithmeticSharedRingTensor through arithmetic secret sharing techniques that distribute data into two shares for two participating parties.

Architecture

• application
  The application package contains applications implemented using the functionalities of MPCTensorLib. Currently, it supports automatic conversion of plaintext cipher models and privacy-preserving neural network inference.
• common
  The common package includes general utilities and the basic data structures used by this lib, such as network communication, random number generators, and other tools.
• config
  The config package includes the basic configuration and network configuration of MPCTensorLib.
• crypto The crypto package is the core of the lib and includes the privacy computation primitives and protocols.
• tutorials
  The tutorials package contains the usage tutorials of MPCTensorLib.

  License

  MPCTensorLib is based on the MIT license, as described in LICENSE.