TAG: Gradient Attack on Transformer-based Language Models

Jieren Deng, Yijue Wang, Ji Li, Chenghong Wang, Chao Shang, Hang Liu, Sanguthevar Rajasekaran, Caiwen Ding


Abstract
Although distributed learning has increasingly gained attention in terms of effectively utilizing local devices for data privacy enhancement, recent studies show that publicly shared gradients in the training process can reveal the private training data (gradient leakage) to a third-party. We have, however, no systematic understanding of the gradient leakage mechanism on the Transformer based language models. In this paper, as the first attempt, we formulate the gradient attack problem on the Transformer-based language models and propose a gradient attack algorithm, TAG, to reconstruct the local training data. Experimental results on Transformer, TinyBERT4, TinyBERT6 BERT_BASE, and BERT_LARGE using GLUE benchmark show that compared with DLG, TAG works well on more weight distributions in reconstructing training data and achieves 1.5x recover rate and 2.5x ROUGE-2 over prior methods without the need of ground truth label. TAG can obtain up to 90% data by attacking gradients in CoLA dataset. In addition, TAG is stronger than previous approaches on larger models, smaller dictionary size, and smaller input length. We hope the proposed TAG will shed some light on the privacy leakage problem in Transformer-based NLP models.
Anthology ID:
2021.findings-emnlp.305
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2021
Month:
November
Year:
2021
Address:
Punta Cana, Dominican Republic
Venue:
Findings
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
3600–3610
Language:
URL:
https://aclanthology.org/2021.findings-emnlp.305
DOI:
10.18653/v1/2021.findings-emnlp.305
Bibkey:
Cite (ACL):
Jieren Deng, Yijue Wang, Ji Li, Chenghong Wang, Chao Shang, Hang Liu, Sanguthevar Rajasekaran, and Caiwen Ding. 2021. TAG: Gradient Attack on Transformer-based Language Models. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 3600–3610, Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
TAG: Gradient Attack on Transformer-based Language Models (Deng et al., Findings 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.findings-emnlp.305.pdf
Video:
 https://aclanthology.org/2021.findings-emnlp.305.mp4
Data
CoLAGLUESST