🔒 Privacy Exposure Analyzer

A comprehensive privacy risk assessment and breach analysis system with a modern web interface.

🚀 Quick Start Guide

Prerequisites

• Python 3.9 or higher
• macOS, Linux, or Windows

Step 1: Clone/Download the Project

# If you have the project folder, navigate to it
cd /path/to/PrivacyExposure

Step 2: Create Virtual Environment

# Create virtual environment
python3 -m venv venv

# Activate virtual environment
# On macOS/Linux:
source venv/bin/activate
# On Windows:
# venv\Scripts\activate

Step 3: Install Dependencies

# Install required packages
pip install streamlit pandas plotly numpy requests

# Install additional dependencies
pip install blinker click toml tornado altair cachetools gitpython pillow protobuf tenacity

Step 4: Run the Application

# Start the Streamlit web application
python -m streamlit run streamlit_app.py --server.port 8501 --server.address 0.0.0.0 --server.headless true

Step 5: Access the Application

Open your web browser and navigate to:

• Local URL: http://localhost:8501
• Network URL: http://0.0.0.0:8501

📊 What the Application Does

🔍 Email Analysis

• Enter an email address to check for data breaches
• Get comprehensive breach analysis and risk assessment

📝 Text Analysis

• Paste text containing personal information
• System extracts and analyzes PII (names, emails, phone numbers, etc.)
• Provides privacy risk assessment

🔬 Comprehensive Analysis

• Full privacy risk assessment with email + additional personal data
• Most thorough analysis available

🛠️ Troubleshooting

If Streamlit asks for email during setup:

# Skip the welcome screen by using headless mode
python -m streamlit run streamlit_app.py --server.headless true

If you get import errors:

# Make sure virtual environment is activated
source venv/bin/activate

# Reinstall dependencies
pip install -r requirements.txt

If port 8501 is busy:

# Use a different port
python -m streamlit run streamlit_app.py --server.port 8502

📁 Project Structure

PrivacyExposure/
├── 📄 README.md              # This file
├── 📄 HOW_TO_RUN.md          # Detailed setup instructions
├── 🐍 main_analysis.py       # Main analysis script
├── 🐍 run_analysis.py        # Analysis runner
├── 🌐 streamlit_app.py       # Web application (MAIN APP)
├── 📋 requirements.txt       # Dependencies
├── 📁 notebooks/
│   └── 📓 ner_extraction.ipynb
├── 📁 privacy_score/         # Core modules
│   ├── __init__.py
│   ├── api_clients.py        # API integration
│   ├── ml_model_fixed.py     # ML model
│   ├── ner_extractor.py      # Text analysis
│   └── scoring.py            # Main scoring engine
└── 📁 venv/                  # Virtual environment

🔧 Alternative Running Methods

Method 1: Direct Python Execution

# Run the main analysis script
python main_analysis.py

# Run the analysis runner
python run_analysis.py

Method 2: Jupyter Notebook

# Start Jupyter
jupyter notebook

# Open notebooks/ner_extraction.ipynb

📈 Features

• Risk Level Assessment: Low/Medium/High/Critical
• Breach Timeline: When and where data was compromised
• Data Type Analysis: What specific information was exposed
• Future Risk Prediction: ML-powered breach risk forecasting
• Actionable Recommendations: Steps to improve privacy
• Downloadable Reports: JSON format analysis results

⚠️ Privacy Notice

This tool analyzes your data locally and queries public breach databases. No personal information is stored or transmitted to our servers.

🆘 Need Help?

If you encounter any issues:

1. Check that your virtual environment is activated
2. Ensure all dependencies are installed
3. Try running with different port numbers
4. Check the terminal output for error messages

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Ready to analyze your privacy exposure? Start with Step 1 above! 🚀