P-Bench

Code for ACL 2024 paper: P-Bench: A Multi-level Privacy Evaluation Benchmark for Language Models

TODO

We are working on code cleaning and our code for membership inference attacks, data extraction attacks and embedding inversion attacks has been uploaded.

1. Improve code readability (In Progress)
2. Add backdoor attacks on generative LLMs ❌

Support Features

1. Parameter-Efficient Fine-Tuning (🤗PEFT) + PrivacyEngine (Opacus) ✅
2. Membership Inference Attacks ✅
3. Training Data Extraction Attacks ✅
4. Embedding Inversion Attacks ✅

Preparation

1. numpy
2. torch
3. transformers
4. wandb
5. tqdm
6. typing
7. ml-swissknife
8. datasets

Fine-tune LMs with/without Differential Privacy

Our base trainer is put in training_interface.py, you may find all fine-tuning examples for different LMs and tuning methods under examples/.

For instance, bert_cls_p-tuning.py under examples/ includes our implementations for BertForSequenceClassification with PEFT methods (you may also try LoRA) with privacy engine.

Run Attacks

Membership Inference Attacks (MIAs)

For MIAs, we implement the Likelihood Ratio Attack (LiRA) by training multiple (50) shallow LMs. You may refer to run_mia.sh for more details.

For evaluations on MIAs, you may use scripts under eval/MIA/ to calculate AUC scores as well as likelihood.

Training Data Extraction Attacks (DEAs)

To run DEAs， the first thing to do is to modify the canary_type_list, insert_proportion_list, and insert_time_base_list in the config.py file to control the type, proportion, and frequency of canary insertions. Then you can run eval/DEA/canary_experiments.py or run the examples under examples/ for GPT or T5 training. Then run the canary_evaluate function provided by the trainer class to calculate exposures. We also provide some plotting functions for comparing different models in eval/DEA/canary_experiments.py.

Embedding Inversion Attacks (EIAs)

To run EIAs, you may first fine-tune LMs with/without DP under examples/. Then you can use attacker code inside eval/EIA/ to train the attacker and perform evaluation.

Citation

Please kindly cite the following paper if you found our method and resources helpful!

@misc{li2024privlmbench,
      title={PrivLM-Bench: A Multi-level Privacy Evaluation Benchmark for Language Models}, 
      author={Haoran Li and Dadi Guo and Donghao Li and Wei Fan and Qi Hu and Xin Liu and Chunkit Chan and Duanyi Yao and Yuan Yao and Yangqiu Song},
      year={2024},
      eprint={2311.04044},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

Miscellaneous

Please send any questions about the code and/or the algorithm to [email protected]