Jieren Deng, Yijue Wang, Ji Li, Chenghong Wang, Chao Shang, Hang Liu, Sanguthevar Rajasekaran, Caiwen Ding
| Challenge: | Recent studies show that publicly shared gradients in the training process can reveal the private training data to a third-party. |
| Approach: | They propose a gradient attack algorithm to reconstruct the local training data using GLUE benchmarks. |
| Outcome: | The proposed algorithm achieves 1.5x recover rate and 2.5x ROUGE-2 over previous methods without the need of ground truth label. |
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| Challenge: | Recent studies show that distributed machine learning is vulnerable to gradient inversion attacks . a recent study demonstrated the possibility of reconstructing private textual training data using partial gradients . |
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| Challenge: | federated learning could overcome the bottleneck of public text data in large language models . a novel attack method is proposed to fully expose text data from gradients . |
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| Challenge: | Recent advances in NLP often stem from large transformer-based pre-trained models. |
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| Challenge: | Existing pre-trained language models are vulnerable to model extraction attacks . model extraction can cause severe privacy leakage even when victim models are facilitated with state-of-the-art defensive strategies. |
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Zhuo Zhang, Jintao Huang, Xiangjing Hu, Jingyuan Zhang, Yating Zhang, Hui Wang, Yue Yu, Qifan Wang, Lizhen Qu, Zenglin Xu
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Fine-grained Pluggable Gradient Ascent for Knowledge Unlearning in Language Models (2024.emnlp-main)
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| Challenge: | Existing studies on knowledge unlearning focus on computer vision but extend their exploration to other fields. |
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