3 papers
cs.CR2025
PriFFT: Privacy-preserving Federated Fine-tuning of Large Language Models via Hybrid Secret Sharing
Zhichao You, Xuewen Dong, Ke Cheng +5
Fine-tuning large language models (LLMs) raises privacy concerns due to the risk of exposing sensitive training data. Federated learning (FL) mitigates this risk by keeping trainin…
cs.LG2025
Adaptive Backdoor Attacks with Reasonable Constraints on Graph Neural Networks
Xuewen Dong, Jiachen Li, Shujun Li +4
Recent studies show that graph neural networks (GNNs) are vulnerable to backdoor attacks. Existing backdoor attacks against GNNs use fixed-pattern triggers and lack reasonable trig…
cs.CR2025
Local Differential Privacy is Not Enough: A Sample Reconstruction Attack against Federated Learning with Local Differential Privacy
Zhichao You, Xuewen Dong, Shujun Li +3
Reconstruction attacks against federated learning (FL) aim to reconstruct users' samples through users' uploaded gradients. Local differential privacy (LDP) is regarded as an effec…