🔐 Exploring Information Privacy — Cybersecurity Project

A comprehensive toolkit implementing modern information privacy techniques tailored for cybersecurity analysts and compliance teams.

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🚀 Features

• 📛 Data Anonymization — k-anonymity, l-diversity, and t-closeness techniques
• 🔐 Advanced Encryption — Homomorphic encryption & differential privacy
• 📊 Privacy Risk Assessment — Automated scoring of data exposure
• 📜 GDPR Compliance Checker — Article-by-article regulation validation
• 🧠 PII Detection & Classification — Sensitive data recognition
• 🌐 Data Flow Mapping — Visual diagrams of data movement

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🛠️ Installation

Clone the repository:

git clone https://github.com/Willie-Conway/Exploring-Information-Privacy-.git
cd Enformation-Information-Privacy

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🧑‍💻 Cybersecurity Skills Demonstrated

• 🛡️ Data Protection — Applied anonymization techniques (k-anonymity, l-diversity, t-closeness)
• 🔐 Encryption Practice — Built a working homomorphic encryption module
• 📋 Regulatory Compliance — Automated GDPR checks across data handling
• 🔎 Risk Analysis — PII classification & privacy risk scoring
• 📈 Communication — Clear, professional data visualizations for reporting

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🗣️ How to Present This in Interviews

Highlight technical complexity:

“The homomorphic encryption module allows computations directly on encrypted data, preserving confidentiality during processing.”

“Differential privacy introduces mathematically-backed noise to datasets for secure analysis.”

Share business value:

“The GDPR compliance checker could reduce manual audit time by 20+ hours per cycle.”

“Our PII detector identifies vulnerable unencrypted fields before exposure occurs.”

Describe your process:

“I used test-driven development for the anonymization logic.”

“Visualization tools were refined across three iterative feedback rounds.”

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📊 Output Analysis

• ✅ K-Anonymity: Achieved generalization up to 2-anonymity
• ✅ L-Diversity: Validated 2-diversity presence
• ✅ Homomorphic Encryption: Encrypted sum of values (10+20+30), correctly decrypted to 60
• ✅ Differential Privacy: Added controlled noise (e.g., 100 → 104.13)
• ✅ Privacy Risk Assessment: No PII detected in the sample dataset
• ✅ GDPR Compliance: Article-wise compliance report with highlights

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📂 Expected Output

Upon running main.py, you'll see:

=== Exploring Information Privacy ===
Running comprehensive privacy analysis...

📦 Python version: 3.12.x
📁 Working directory: /home/user/Exploring-Information-Privacy
📄 Directory contents: ['main.py', 'data_anonymization', ...]

✅ JSON report saved to: ./reports/privacy_report.json
✅ GDPR heatmap saved to: ./reports/gdpr_compliance.png
✅ Data flow diagram saved to: ./reports/data_flow.png

🎉 Report generation successful!

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🌟 Key Enhancements

🔍 Visual Output

• GDPR compliance heatmap with intuitive coloring
• Network-style data flow diagram
• Output saved to ./reports/ directory

🧩 Main Script Enhancements

• Modular demo execution
• JSON report generation
• Timestamped logs & error handling
• Cross-platform paths with pathlib

📦 Production-Ready Improvements

• Type hints for maintainability
• Config file management
• Separation of concerns (execution vs. reporting)
• Rich docstrings for all modules

👔 Employer-Focused Outputs

• 📸 Visuals (.png): compliance, data flow
• 📄 Machine-readable: privacy_report.json
• 🖥️ Clear console logs

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⚙️ How to Use

1. Make sure your working directory has main.py and required folders
2. Run:

python main.py

3. Check your output directory:

ls -l reports/

Expected files:

• privacy_report.json
• gdpr_compliance.png
• data_flow.png

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🧰 Debugging Tips

If files are missing:

• Check printed paths in console
• Manually test file creation:

with open("test.txt", "w") as f:
    f.write("test")

🛠️ Common Fixes

• ✅ Permission Errors: Uses relative paths inside the project
• ✅ Cross-Platform Support: Compatible with Windows, macOS, Linux
• ✅ Visibility: Prints absolute paths for quick navigation