Open Source AI Project


Secure-ML is a project that focuses on implementing Secure Linear Regression in a Semi-Honest Two-Party Setting.


Secure-ML is a cutting-edge project designed to enhance the security and privacy of machine learning operations, specifically focusing on logistic regression algorithms applied to the MNIST dataset, a widely-used benchmark in the field. The project’s core lies in its ability to perform Secure Linear Regression within a Semi-Honest Two-Party Setting, a crucial advancement for ensuring data privacy in machine learning processes.

Developed using C++, Secure-ML integrates two essential libraries to achieve its objectives: emp-ot (version 0.1) and Eigen (version 3.3.7). Emp-ot is utilized for Oblivious Transfer (OT), a cryptographic protocol critical for maintaining the confidentiality of data exchange between parties. This ensures that during the computation, each party learns only what is essential for the process, and no more, effectively protecting sensitive information. On the other hand, Eigen, known for its efficient matrix operations, is employed to handle the computational demands of logistic regression algorithms, providing fast and accurate processing of the MNIST dataset.

The project targets systems running Ubuntu 20.04 (Linux OS) and necessitates several dependencies, including OpenSSL for robust encryption, Boost for effective networking, and GMP for high-precision arithmetic operations. These components are integral to Secure-ML’s functionality, offering a secure environment for conducting machine learning operations. The project simplifies the installation process through the provision of scripts, facilitating easy setup of the necessary libraries and environments. This approach not only reduces the complexity of getting started with Secure-ML but also makes it accessible to a broader audience.

Secure-ML stands out for its commitment to data privacy and integrity by executing computations in an encrypted domain. This feature is particularly advantageous in scenarios where data sensitivity is a concern, allowing for the secure processing of information without compromising on accuracy or efficiency. Moreover, Secure-ML serves as an invaluable educational resource for individuals keen on exploring the intersection of cryptography and data science. By offering a practical application of secure computing principles in machine learning, it paves the way for further advancements in the field, promoting a deeper understanding of how cryptographic techniques can be leveraged to protect data privacy in an increasingly digital world.

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