This research project demonstrates data memorization risks in Large Language Models (LLMs) and provides effective privacy protection methods. Our findings show that fine-tuning can increase data leakage by up to 60%, while proper protection techniques can reduce it to 0%.

• Data Memorization Confirmed: Fine-tuning with repeated data increases leakage by 60%
• Protection Methods Work: Multiple techniques achieve 0% leakage rate
• Quantifiable Results: Scientific methodology with reproducible metrics
• Real-world Applicability: Tested on modern LLM architectures

git clone https://github.com/yourusername/llm-privacy-research.git
cd llm-privacy-research
pip install -r requirements.txt

# Test data memorization
python experiments/run_memorization_test.py

# Test protection methods
python experiments/run_protection_test.py

# Run comprehensive analysis
python experiments/run_comparative_analysis.py

Baseline leakage: 0.0%
Post-training leakage: 60.0%
Memorization increase: 60.0%
Status: MEMORIZATION CONFIRMED

• Create controlled datasets with traceable "canary" strings
• Test baseline model behavior vs post-training behavior
• Measure exact and partial memorization rates
• Quantify privacy leakage with statistical significance

• Data Deduplication: Remove similar training examples
• Differential Privacy: Add calibrated noise during generation
• Entropy Filtering: Block low-entropy (memorized) outputs
• Content Filtering: Detect and block sensitive patterns

• Compare multiple LLM architectures
• Test with realistic 2025 data patterns
• Measure effectiveness across different attack scenarios
• Provide reproducible experimental setup

├── src/                    # Core research code
├── experiments/            # Standalone experiment scripts
├── notebooks/             # Jupyter notebooks for interactive analysis
├── data/                  # Sample datasets and test cases
├── results/               # Experimental results and visualizations
├── docs/                  # Detailed documentation
└── tests/                 # Unit tests and validation

• Microsoft Phi-3-mini (3.8B parameters) - Latest 2025 model
• Google Gemma-2 (2B parameters) - Google's newest architecture
• GPT-2 (1.5B parameters) - Baseline comparison
• Qwen2.5 (3B parameters) - Alibaba's latest model

• Python 3.8+
• PyTorch 2.0+
• Transformers 4.30+
• scikit-learn 1.0+
• numpy 1.21+
• See requirements.txt for complete list

from src.memorization_analyzer import MemorizationAnalyzer

analyzer = MemorizationAnalyzer(model_name="microsoft/Phi-3-mini-4k-instruct")
results = analyzer.run_analysis()
print(f"Memorization rate: {results['memorization_rate']:.1f}%")

from src.privacy_protector import PrivacyProtector

protector = PrivacyProtector(model_name="microsoft/Phi-3-mini-4k-instruct")
protected_results = protector.demonstrate_protection_effectiveness()

This code accompanies our research paper: "Assessing and Mitigating Data Memorization Risks in Fine-Tuned Large Language Models"

@article{llm_privacy_2025,
    title={Assessing and Mitigating Data Memorization Risks in Fine-Tuned Large Language Models},
    author={Your Name},
    journal={arXiv preprint},
    year={2025}
}

1. Fork the repository
2. Create a feature branch (git checkout -b feature/new-protection-method)
3. Commit your changes (git commit -am 'Add new protection method')
4. Push to the branch (git push origin feature/new-protection-method)
5. Create a Pull Request

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

• Transformers library by Hugging Face
• PyTorch framework
• Research inspired by privacy-preserving ML literature
• Special thanks to the open-source AI community

• Privacy in Machine Learning
• Differential Privacy for Deep Learning
• LLM Security Research

⭐ Star this repository if it helped your research! 🔄 Fork to contribute your own privacy protection methods!