Secure Machine Learning

Secure Linear Regression in the Semi-Honest Two-Party Setting. More details on the protocol can be found in the SecureML paper.

Prerequisites

1. emp-ot.
2. Eigen 3.3.7.

Building Secure-ML

git clone https://github.com/shreya-28/Secure-ML.git
cd Secure-ML
mkdir build
cd build
cmake ..
make

Running Secure-ML

The build system creates two binaries, namely, ideal_functionality and secure_ML. The former represents the functionality that the latter implements securely.
The binaries can be executed as follows:

• ideal_functionality
  • ./build/bin/ideal_functionality [num_iter]

• secure_ML
  • On local machine
    • ./build/bin/secure_ML 1 8000 [num_iter] & ./build/bin/secure_ML 2 8000 [num_iter]
  • On two different machines
    • ./build/bin/secure_ML 1 8000 [num_iter] on Machine 1
    • ./build/bin/secure_ML 2 8000 [num_iter] [addr_of_machine_1] on Machine 2