Data Anonymizer Tool

A powerful and flexible tool for anonymizing datasets using Differential Privacy and K-Anonymity techniques with a modern Streamlit GUI.

Features

• 🔒 Differential Privacy: Apply mathematically rigorous Laplace or Gaussian noise to numerical columns
• 🎭 K-Anonymity: Anonymize categorical data using suppression, generalization, or synthetic data generation
• 🖥️ Modern GUI: Interactive Streamlit web interface for easy data upload and configuration
• ⚙️ Flexible Configuration: Use predefined privacy templates or customize settings
• 📊 Visual Analytics: Compare original vs anonymized data with interactive charts
• 📋 Comprehensive Reporting: Detailed anonymization reports and utility metrics
• 🔧 Multiple Interfaces: GUI, CLI, and Python library interfaces
• 📈 Privacy Templates: Pre-configured settings for different privacy levels

Installation

# Clone the repository
git clone <repository-url>
cd data-anonymizer

# Install dependencies
pip install -r requirements.txt

# Install the package
pip install .

Usage

🖥️ Streamlit GUI (Recommended)

Launch the interactive web interface:

streamlit run data_anonymizer/gui.py

The GUI provides:

• 📁 Easy file upload - Drag and drop CSV files
• ⚙️ Interactive controls - Adjust epsilon and k-values with sliders
• 🎯 Column selection - Choose which columns to anonymize
• 📊 Live previews - See your data before and after anonymization
• 📥 One-click download - Export anonymized data instantly

💻 Command-Line Interface

data-anonymizer input.csv output.csv --numerical_cols age salary --quasi_identifiers zipcode --k 5 --epsilon 1.0

Options:

• --numerical_cols: Columns for differential privacy
• --quasi_identifiers: Columns for k-anonymity
• --epsilon: Privacy budget (lower = more private)
• --k: Minimum group size for k-anonymity
• --strategy: K-anonymity strategy (suppression, generalization, synthetic)
• --template: Use privacy template (high_privacy, medium_privacy, low_privacy)

🐍 Python Library

from data_anonymizer import DataAnonymizer

# Initialize the anonymizer
anonymizer = DataAnonymizer(random_seed=42)

# Load data
anonymizer.load_data("input.csv")

# Apply differential privacy to numerical columns
anonymizer.apply_differential_privacy(
    numerical_columns=['age', 'salary'], 
    epsilon=1.0
)

# Apply k-anonymity to categorical columns
anonymizer.apply_k_anonymity(
    quasi_identifiers=['zipcode', 'gender'], 
    k=5, 
    strategy='generalization'
)

# Save anonymized data with report
anonymizer.save_anonymized_data("output.csv", include_report=True)

# Get detailed analytics
report = anonymizer.get_anonymization_report()
print(report)

Privacy Templates

Choose from predefined privacy levels:

• 🔒 High Privacy: ε=0.1, k=5, synthetic strategy
• ⚖️ Medium Privacy: ε=1.0, k=3, generalization strategy
• 🔓 Low Privacy: ε=2.0, k=2, suppression strategy
• 🎓 Research Compliant: ε=0.5, k=3, balanced approach
• ⚡ Minimal: ε=5.0, k=2, minimal anonymization

Sample Data Generation

The tool includes a powerful sample data generator to create realistic datasets for testing.

Generate all sample datasets:

python -m data_anonymizer.sample_generator

Generate a specific dataset:

python -m data_anonymizer.sample_generator --dataset medical --records 500

Available datasets:

• employees: Corporate employee data
• customers: Retail customer data
• medical: Sensitive patient data (HIPAA-like)
• financial: Financial account data

Testing

Run the comprehensive test suite:

# Install test dependencies
pip install pytest

# Run all tests
pytest

# Run with coverage
pytest --cov=data_anonymizer

Project Structure

data_anonymizer/
├── core/                 # Core anonymization logic
│   ├── anonymizer.py    # Main anonymizer class
│   ├── privacy.py       # Differential privacy implementation
│   └── kanonymity.py    # K-anonymity implementation
├── config/              # Configuration management
│   └── settings.py      # Settings and templates
├── gui.py              # Streamlit web interface
└── cli.py              # Command-line interface

tests/                   # Test suite
├── test_anonymizer.py
├── test_privacy.py
├── test_kanonymity.py
└── test_config.py

Contributing

1. Fork the repository
2. Create a feature branch
3. Add tests for new functionality
4. Run the test suite
5. Submit a pull request

License

This project is licensed under the MIT License.

Citation

If you use this tool in your research, please cite:

@software{data_anonymizer,
  title={Data Anonymizer Tool},
  author={Christophe Amoussouvi},
  year={2025},
  url={https://github.com/jace-solutions/data-anonymizer}
}