@inproceedings{zhang-etal-2024-revisiting,
    title = "Revisiting Data Reconstruction Attacks on Real-world Dataset for Federated Natural Language Understanding",
    author = "Zhang, Zhuo  and
      Huang, Jintao  and
      Hu, Xiangjing  and
      Zhang, Jingyuan  and
      Zhang, Yating  and
      Wang, Hui  and
      Yu, Yue  and
      Wang, Qifan  and
      Qu, Lizhen  and
      Xu, Zenglin",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.1227",
    pages = "14080--14091",
    abstract = "With the growing privacy concerns surrounding natural language understanding (NLU) applications, the need to train high-quality models while safeguarding data privacy has reached unprecedented importance. Federated learning (FL) offers a promising approach to collaborative model training by exchanging model gradients. However, many studies show that eavesdroppers in FL could develop sophisticated data reconstruction attack (DRA) to accurately reconstruct clients{'} data from the shared gradients. Regrettably, current DRA methods in federated NLU have been mostly conducted on public datasets, lacking a comprehensive evaluation of real-world privacy datasets. To address this limitation, this paper presents a pioneering study that reexamines the performance of these DRA methods as well as corresponding defense methods. Specifically, we introduce a novel real-world privacy dataset called FedAttack which leads to a significant discovery: existing DRA methods usually fail to accurately recover the original text of real-world privacy data. In detail, the tokens within a recovery sentence are disordered and intertwined with tokens from other sentences in the same training batch. Moreover, our experiments demonstrate that the performance of DRA is also influenced by different languages and domains. By discovering these findings, our work lays a solid foundation for further research into the development of more practical DRA methods and corresponding defenses.",
}

With the growing privacy concerns surrounding natural language understanding (NLU) applications, the need to train high-quality models while safeguarding data privacy has reached unprecedented importance. Federated learning (FL) offers a promising approach to collaborative model training by exchanging model gradients. However, many studies show that eavesdroppers in FL could develop sophisticated data reconstruction attack (DRA) to accurately reconstruct clients{'} data from the shared gradients. Regrettably, current DRA methods in federated NLU have been mostly conducted on public datasets, lacking a comprehensive evaluation of real-world privacy datasets. To address this limitation, this paper presents a pioneering study that reexamines the performance of these DRA methods as well as corresponding defense methods. Specifically, we introduce a novel real-world privacy dataset called FedAttack which leads to a significant discovery: existing DRA methods usually fail to accurately recover the original text of real-world privacy data. In detail, the tokens within a recovery sentence are disordered and intertwined with tokens from other sentences in the same training batch. Moreover, our experiments demonstrate that the performance of DRA is also influenced by different languages and domains. By discovering these findings, our work lays a solid foundation for further research into the development of more practical DRA methods and corresponding defenses.
Zhang, Zhuo Huang, Jintao Hu, Xiangjing Zhang, Jingyuan Zhang, Yating Wang, Hui Yu, Yue Wang, Qifan Qu, Lizhen Xu, Zenglin
Revisiting Data Reconstruction Attacks on Real-world Dataset for Federated Natural Language Understanding