AnonyMate 0.1.6
A comprehensive toolkit for data anonymization, masking, and encryption.
Requires Python: >=3.6

# AnonyMate

**AnonyMate** is a Python toolkit that provides a powerful suite of tools for data anonymization, masking, encryption, and fake data generation. Designed for developers working with sensitive information, AnonyMate helps ensure compliance with data privacy regulations (e.g., GDPR, HIPAA) while simplifying the anonymization process for testing, analysis, and data sharing.

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## Key Features

- **Data Masking**: Mask sensitive data (e.g., names, emails, IDs) with custom characters, ensuring data cannot be directly identified.
- **Data Hashing**: One-way hashing for sensitive data using SHA-256, providing a non-reversible transformation to securely store confidential data.
- **Encryption & Decryption**: Securely encrypt and decrypt sensitive data using symmetric encryption, allowing data to be accessed only by authorized parties.
- **Synthetic Data Generation**: Generate realistic fake data (e.g., names, emails, addresses) for testing and development without exposing actual sensitive data.
- **Partial Masking and Validation**: Support for partial masking (e.g., "123-***-789") for specific use cases and simple validation tools for email formats.
- **Compliance-Friendly**: Assists organizations in meeting data privacy and security requirements, making it suitable for various industries, including healthcare, finance, and e-commerce.

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## Installation

Install AnonyMate with pip:

```bash
pip install AnonyMate
```

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## Quick Start

Hereâ€™s how to get started using AnonyMate for basic anonymization, encryption, and fake data generation.

Basic Setup

```python
from anonymate.anonymizer import Anonymizer
from anonymate.faker_util import generate_fake_name, generate_fake_email, generate_fake_address

# Initialize Anonymizer
anonymizer = Anonymizer()

# Mask, hash, encrypt, and decrypt data
masked = anonymizer.mask_text("SensitiveData")
hashed = anonymizer.hash_text("SensitiveData")
encrypted = anonymizer.encrypt_text("SensitiveData")
decrypted = anonymizer.decrypt_text(encrypted)

# Generate fake data
fake_name = generate_fake_name()
fake_email = generate_fake_email()
fake_address = generate_fake_address()

# Display results
print("Masked:", masked)
print("Hashed:", hashed)
print("Encrypted:", encrypted)
print("Decrypted:", decrypted)
print("Fake Name:", fake_name)
print("Fake Email:", fake_email)
print("Fake Address:", fake_address)
```


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## Example Use Cases
1. Healthcare Applications: Mask and encrypt patient information to ensure HIPAA compliance and secure sharing with researchers.
2. Financial Services: Anonymize and hash transaction data to meet GDPR and other privacy regulations.
3. Data Science and Machine Learning: Use synthetic data for model training, ensuring no real data is exposed during testing and validation.
4. Customer Support Systems: Anonymize customer data (e.g., names, phone numbers) before sharing with support teams or contractors.
5. Social Media Analytics: Mask user data before running analysis on social media usage to ensure compliance with data privacy standards.

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## License

### MIT License

This project is licensed under the MIT License, allowing free use, modification, and distribution with attribution.

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## Contributing
Contributions are welcome! Please open issues or submit pull requests for bug fixes, improvements, or new features.

To contribute:

- Fork the repository.
- Create a new branch (git checkout -b feature-branch).
- Make your changes and commit (git commit -m 'Add new feature').
- Push to the branch (git push origin feature-branch).
- Open a Pull Request.

Thank you for your interest in improving AnonyMate!