README.md

metadata

language:
  - en
library_name: peft
pipeline_tag: text-generation
base_model: EleutherAI/pythia-160m
base_model_relation: adapter
datasets:
  - cc0de/Enron_email
tags:
  - lora
  - privacy
  - pii
  - membership-inference
  - masked-fine-tuning

TMFT: Targeted Masked Fine-Tuning

This project tests whether masking privacy-sensitive token losses during LoRA fine-tuning reduces PII memorization with less utility degradation than random masking. It is an empirical mitigation study, not differential privacy or machine unlearning.

Vessel Setup

Upload the entire tmft_project/ directory and open a terminal in that directory.

python -m pip install -U pip
python -m pip uninstall -y transformers peft accelerate tokenizers huggingface_hub datasets
python -m pip install -r requirements.txt
python -m spacy download en_core_web_sm

Restart the Jupyter kernel after installation. The tested compatibility stack uses PyTorch 2.3.1, Transformers 4.41.2, PEFT 0.11.1, and Datasets 2.20.0.

End-to-End Experiment

Prepare real PII-containing Enron splits and a real prefix-target evaluation set:

python main.py --mode prepare --force_prepare

Train all conditions:

python main.py --mode train --method all

Evaluate TER, SER, held-out perplexity, MDP, Loss-MIA AUC, and Min-K MIA AUC:

python main.py --mode eval --method all

Generate result figures:

python main.py --mode plot

The full pipeline can be launched with:

python main.py --mode all --method all --force_prepare

For an interactive run, execute tmft_experiment.ipynb from top to bottom.

Experimental Conditions

• baseline: standard LoRA fine-tuning
• rmft: random 15% loss masking
• tmft_ner: loss masking at spaCy plus regex PII spans
• tmft_mia: online token masking where the current model is more confident than the frozen base model
• tmft_combined: union of NER and post-warm-up MIA masks

Outputs

• data/processed/: train, validation, and test DatasetDict
• data/pii_eval.json: automatically generated real PII prefix-target attacks
• results/<method>/: LoRA adapters and training metadata
• results/tables/main_results.csv: submission-ready numeric table
• results/figures/: PNG and PDF privacy/utility figures

Do not report results if preprocessing prints a synthetic fallback warning. The final config disables fallback so an unavailable real dataset fails loudly.

Hugging Face Upload

huggingface-cli login
python main.py --mode upload --method tmft_combined \
  --hf_repo_id YOUR_USERNAME/tmft-pythia-160m-tmft-combined

Use --public only after checking that the saved artifacts contain no raw PII.