🛡️ PREVANALYZER — Data Privacy & Protection Tool

Prevanalyzer is a Flask-based data privacy and protection web application that allows users to upload datasets, apply privacy-preserving techniques (masking, hashing, encryption, tokenization), and get guidance via an AI-powered chatbot. Designed for secure, beginner-friendly data protection.

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✨ Features

• Secure File Uploads – CSV, Excel, JSON
• Data Protection Techniques
  • Masking (partial data hiding)
  • Hashing (irreversible transformation)
  • Encryption (AES-based)
  • Tokenization (safe value replacement)
• Column-wise Protection Selection
• Custom Python Code Editor for advanced transformations
• Protected Data Preview & Download
• AI Chatbot Assistance
  • Privacy tips & guidance
  • Dataset-aware Q&A (powered by Groq LLM)
• Database Logging
  • Upload history
  • Applied protection methods

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🚀 Quick Start

# Clone the repository
git clone https://github.com/YOUR_USERNAME/prevanalyzer.git
cd prevanalyzer

# Create virtual environment
python -m venv venv
# Activate
# Windows
venv\Scripts\activate
# macOS / Linux
source venv/bin/activate

# Install dependencies
pip install -r requirements.txt

# Initialize database
python database.py

# Run the Flask app
python app.py

📸 Usage

1. Upload a CSV / Excel / JSON file
2. Select protection techniques for each column
3. (Optional) Apply custom Python transformation logic
4. Preview protected data
5. Download processed file or PDF report
6. Ask privacy-related questions via the AI chatbot

🛠️ Tech Stack

• Backend: Python, Flask
• Data Processing: Pandas
• Security: AES Encryption, SHA-256 Hashing
• Database: SQLite
• Frontend: HTML, CSS, Bootstrap
• AI Chatbot: Groq API (LLaMA models)

📁 Project Structure

prevanalyzer/ │── app.py # Main Flask application

│── database.py # SQLite setup

│── connection.py # Groq chatbot integration

│── templates/ # HTML templates

│── uploads/ # Uploaded & processed files

│── data.db # SQLite database

│── requirements.txt # Dependencies

│── .env # API keys (ignored in Git)

│── .gitignore

🎯 Use Cases

• Students learning data privacy & anonymization
• Data analysts securing datasets before analysis
• Organizations protecting sensitive customer data
• General users sharing files safely

🎨 Future Enhancements

• User authentication (login/signup)
• Advanced anonymization techniques
• Role-based access control
• Cloud deployment (AWS / GCP)
• Privacy analytics dashboard

🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Open a pull request

💡 Contributions, issues, and feature requests are welcome!

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