🛡️ DataArmor

DataArmor is a Python-based data anonymization tool that uses the Mondrian algorithm to achieve k-anonymity on structured datasets. Designed with a simple GUI, it allows users to protect sensitive information by anonymizing quasi-identifiers through generalization and intelligent partitioning.

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📌 Features

• ✅ Mondrian algorithm for multi-dimensional k-anonymity
• ✅ Intuitive GUI using Tkinter
• ✅ Supports numeric and categorical quasi-identifiers
• ✅ Easy CSV input and anonymized output
• ✅ Suitable for research and educational purposes

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GUI Overview

• Load your dataset (CSV format)
• Select:
  • Explicit Identifier (EID)
  • Quasi-Identifiers (QIDs): Numeric and Non-numeric
  • Desired value of k
• Click "Anonymize" to apply k-anonymity
• View and save the anonymized dataset

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How It Works

DataArmor uses the Mondrian multi-dimensional partitioning algorithm:

1. Partitioning: Recursively splits the dataset based on QID values.
2. Validation: Ensures each partition has at least k records.
3. Generalization: Applies generalization or suppression to satisfy anonymity requirements.

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File Structure

File	Description
DataArmor_GUI.py	Main script with Tkinter GUI
k_Anonymity.py	Mondrian algorithm implementation
data1.csv	Sample dataset
README.md	Project documentation

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Requirements

• Python 3.x
• pandas
• tkinter (standard with Python)
• csv (built-in)

Install dependencies (if needed):

pip install pandas

Getting Started

1. Clone the repository:

git clone https://github.com/Maha028/DataArmor.git cd DataArmor

2. Run the GUI:

python DataArmor_GUI.py

3. Follow the GUI steps to load your dataset and apply k-anonymity.

Sample Dataset

The repository includes data1.csv, a sample dataset for testing. You can replace it with your own structured CSV files.