🔐 Python Encryption & Hashing

This repository demonstrates encryption, decryption, and hashing techniques in Python using Google Colab.
It includes practical examples of the CIA Triad (Confidentiality, Integrity, Availability) and the Avalanche Effect in hashing.

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✨ Features

✅ Symmetric Encryption & Decryption using Fernet (AES)
✅ File Hashing with MD5, SHA-1, and SHA-256
✅ Demonstration of the Avalanche Effect 🔄
✅ Practical verification of the CIA Triad
✅ Simple, easy-to-understand Colab Notebook

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📂 Files

File	Description
python_encryption_hashing.ipynb	Jupyter/Colab notebook containing all encryption, decryption, and hashing code
file.txt	Example input file (original)
file_modified.txt	Modified file for Avalanche Effect demonstration
encrypted_file.txt	Encrypted version of file.txt (ciphertext)
decrypted_file.txt	Decrypted version of the encrypted file (should match original)
hashes.txt	Saved hash values (MD5, SHA-1, SHA-256)

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🛠️ Requirements

• Python 3.x\
• cryptography\
• Built-in hashlib

Install dependencies:

pip install cryptography

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🚀 How to Run

1. Open the notebook in Google Colab or Jupyter.\
2. Upload a sample file.txt to your Drive.\
3. Run the notebook cells to:
   • Encrypt & decrypt files 🔑
   • Generate hash values 🧾
   • Compare original vs modified file hashes 🔍

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📊 Example Output

Original File Hashes:

MD5:    8dd986dcec9ba31cd6ffc6624f84ba43
SHA1:   b2d7e568fa8948cd63437e846d322810e981849f
SHA256: 7ac32839f53fe7e60cfe681deead1c0368c3aa6e755383e54de4be3e4ae64119

Modified File Hashes:

MD5:    d39cc9b6e527e16f00ab63d7c4eb669
SHA1:   bfdc732a9e7239e3c8c6fba33ac4997627fb4e17
SHA256: fea1d5409f913c98b6b75fbab8c172d7fb2d45cfbe7d1d8f8fa52e6dc3726ed

🔄 Comparison: All values are different → proves Avalanche Effect ⚡

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🌐 CIA Triad Demonstration

• Confidentiality → Encryption ensures only key holders can access the data.\
• Integrity → Hashing ensures detection of even the smallest modification.\
• Availability → Files accessible anytime via Google Drive + Colab.

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📌 Avalanche Effect

A tiny change in input (e.g., adding one word to a file) causes a completely different hash output.
This property ensures high sensitivity of hashing functions to data integrity.

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👨‍💻 Author

Hassan Raza
BS Artificial Intelligence -- Security & AI Enthusiast

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⭐ If you find this useful, don't forget to star the repo!