most citedLocal Differential Privacy is Not Enough: A Sample Reconstruction Attack against Federated Learning with Local Differential Privacy

10 citations · 15 across the 3 of their papers we have counts for

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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.CR2025★ 10 cited

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…