Homomorphic Encryption and Machine Learning for Cybersecurity Overview This repository contains code examples and research materials exploring the fusion of homomorphic encryption and machine learning for privacy-preserving data analysis in cybersecurity. The research aims to address the challenges of data breaches and enhance data security through innovative cryptographic and data science techniques.

Key Features Demonstrates homomorphic encryption using the Paillier cryptosystem in Python. Integrates machine learning algorithms for enhanced data analysis while maintaining data privacy. Investigates the potential of encrypted data processing in cybersecurity applications. Provides code snippets, experiments, and insights into combining encryption and machine learning for improved data security. Installation Clone the repository to your local machine: git clone https://github.com/Isuleim/homomorphic-encryption-cybersecurity.git Install the required Python packages: pip install phe # For homomorphic encryption

Add other necessary packages here

pip install phe # For homomorphic encryption

Add other necessary packages here

pip install phe # For homomorphic encryption

Add other necessary packages here

pip install phe # For homomorphic encryption

Add other necessary packages here

Usage Navigate to the project directory: cd homomorphic-encryption-cybersecurity Run the Python scripts to explore homomorphic encryption and machine learning examples: python homomorphic_encryption_example.py

Add other script names and descriptions here

python homomorphic_encryption_example.py

Add other script names and descriptions here

python homomorphic_encryption_example.py

Add other script names and descriptions here

Contributing Contributions to this project are welcome! Feel free to submit pull requests or open issues to suggest improvements, report bugs, or discuss new ideas. License This project is licensed under the MIT License - see the LICENSE file for details.