Privacy Protection Redaction LLM 🛡️

Welcome to the Privacy Protection Redaction LLM repository! This project focuses on leveraging deep learning techniques to protect sensitive data through redaction. Here, we will explore how to implement effective privacy protection measures using advanced models from Hugging Face and PyTorch.

Table of Contents

1. Introduction
2. Features
3. Technologies Used
4. Installation
5. Usage
6. Examples
7. Contributing
8. License
9. Contact
10. Releases

Introduction

In today's digital age, data privacy is more important than ever. The Privacy Protection Redaction LLM aims to provide a robust solution for redacting personally identifiable information (PII) from various text sources. By using state-of-the-art transformer models, we can automate the process of identifying and removing sensitive information, ensuring compliance with data protection regulations.

Features

• AI-Powered Redaction: Utilizes deep learning models to identify and redact PII.
• Flexible Integration: Easily integrate with existing workflows and applications.
• Customizable Models: Fine-tune models based on specific use cases.
• User-Friendly Interface: Built with Jupyter Notebooks for easy experimentation.
• Open Source: Free to use and modify according to your needs.

Technologies Used

This project incorporates several cutting-edge technologies:

• AI: Artificial Intelligence for intelligent data processing.
• CUDA: For accelerated computations on NVIDIA GPUs.
• Deep Learning: Utilizing neural networks for complex data analysis.
• Hugging Face Transformers: A library for state-of-the-art NLP models.
• Jupyter Notebook: An interactive environment for data science.
• PyTorch: A flexible deep learning framework.
• NLP: Natural Language Processing techniques for text analysis.

Installation

To get started, clone the repository and install the required dependencies. Use the following commands:

git clone https://github.com/JuanDiego-10/Privacy_Protection_Redaction_LLM.git
cd Privacy_Protection_Redaction_LLM
pip install -r requirements.txt

Make sure you have Python 3.6 or higher installed. You will also need to have CUDA set up if you plan to use GPU acceleration.

Usage

Once you have installed the repository, you can start using it in your Jupyter Notebook. Here's a basic example of how to use the redaction functionality:

from redaction_model import Redactor

# Initialize the redactor
redactor = Redactor()

# Sample text containing PII
text = "My name is John Doe and my email is john.doe@example.com."

# Perform redaction
redacted_text = redactor.redact(text)

print(redacted_text)

This will output the text with sensitive information redacted.

Examples

Example 1: Basic Redaction

You can start with simple text inputs to see how the model performs:

text = "Contact me at jane.smith@gmail.com."
redacted_text = redactor.redact(text)
print(redacted_text)  # Output: "Contact me at [REDACTED]."

Example 2: Batch Processing

The model can also handle multiple texts at once:

texts = [
    "My phone number is 123-456-7890.",
    "My address is 123 Main St, Springfield."
]
redacted_texts = redactor.redact_batch(texts)
print(redacted_texts)  # Outputs redacted texts for each input.

Contributing

We welcome contributions to improve the Privacy Protection Redaction LLM. Please follow these steps:

1. Fork the repository.
2. Create a new branch (git checkout -b feature-branch).
3. Make your changes.
4. Commit your changes (git commit -m 'Add new feature').
5. Push to the branch (git push origin feature-branch).
6. Open a pull request.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Contact

For any questions or feedback, feel free to reach out:

• Juan Diego: juan.diego@example.com

Releases

To download the latest release, visit the Releases section. Make sure to download the necessary files and execute them as per the instructions provided.

Explore the capabilities of the Privacy Protection Redaction LLM and contribute to making data privacy a priority in your applications.