SecuraX is an AI-powered platform that enables law enforcement agencies to leverage the power of cutting-edge technologies like Zero-Knowledge Proofs, Federated Learning, and Large Language Models (LLMs) while ensuring data privacy and civil liberties.
- Zero-Knowledge Proofs: Analyze data without exposing sensitive information.
- Federated Learning: Collaboratively train AI models while keeping data on-premises.
- Pseudonymization: Anonymize and mask personal data in documents with flexible retrieval options.
- Streamlit Frontend: Intuitive web-based interface for seamless user interaction.
- Python 3.7+
- Poetry (Python package and dependency manager)
- Docker (optional, for containerized deployment)
- Clone the repository:
git clone https://github.com/your-username/SecuraX.git- Navigate to the project directory:
cd SecuraX- Install dependencies using Poetry:
poetry install- Start the Streamlit frontend:
poetry run streamlit run app.py- Access the application in your web browser at
http://localhost:8501.
- Build the Docker image:
docker build -t securax .- Run the Docker container:
docker run -p 8501:8501 securaxNote: You can use an already existing version of this image:
docker pull p1utoze/securax:v1.0
- Access the application in your web browser at
http://localhost:80.
We welcome contributions from the community! Please fork the repository, create a new branch, and submit a pull request with your changes.
This project is licensed under the MIT License.
- Streamlit for the intuitive frontend framework.
- Poetry for dependency management.
- Docker for containerization.
This README.md provides an overview of the SecuraX project, including its features, installation instructions for local and Docker-based deployment, contribution guidelines, and licensing information. It also acknowledges the key technologies and tools used in the project.