Version: 0.0.1
Last Updated: September 3, 2023
Authors: Memento Research Team
- Introduction
- Mission
- Vision
- Goals and Objectives
- Project Overview
- Technologies Used
- Team
- Project Structure
Welcome to Memento Research, a cutting-edge student organization formed as a thesis project in Software Engineering. Our primary goal is to develop a Convolutional Neural Network (CNN) integrated with Fully Homomorphic Encryption (FHE) capabilities to identify the presence of humans in images. We leverage state-of-the-art libraries like Concrete ML from Zama to achieve this ambitious objective.
Our mission is to advance the field of computer vision and privacy-preserving machine learning by developing a robust and secure solution for human presence detection in images. We aim to contribute to the development of technology that respects individual privacy while maintaining the highest levels of accuracy.
Memento Research envisions a world where AI-driven image analysis can be performed securely and ethically. We aspire to create a framework that can be extended to various domains, including surveillance, healthcare, and autonomous vehicles, while safeguarding data privacy.
- Develop a CNN model that can accurately detect the presence of humans in images.
- Implement Fully Homomorphic Encryption (FHE) to ensure data privacy during image analysis.
- Integrate Concrete ML from Zama to enhance the efficiency and security of our model.
- Publish research findings and contribute to the academic and open-source communities.
- Foster a collaborative and innovative research environment for students in the field of Software Engineering.
Our project involves the creation of a CNN-based model that can analyze images while keeping the data fully encrypted using FHE. This model will be capable of identifying the presence of humans in images, making it suitable for various applications where privacy is a concern.
We leverage a combination of cutting-edge technologies and libraries, including:
- Python
- PyTorch
- Concrete ML from Zama
- Fully Homomorphic Encryption (FHE) libraries
- Convolutional Neural Networks (CNN)
- Git and GitHub for version control
Meet our dedicated team of software engineering enthusiasts and researchers:
Our project is organized into the following key components:
CNN_Model: Contains the code for developing and training the Convolutional Neural Network.FHE_Implementation: Includes the implementation of Fully Homomorphic Encryption to secure image data.Zama_ConcreteML: Integration of Concrete ML from Zama into our project.Research_Papers: Research papers and documentation related to our findings.