3 papers
cs.LG2026
Differentially Private Subspace Fine-Tuning for Large Language Models
Lele Zheng, Xiang Wang, Tao Zhang +3
Fine-tuning large language models on downstream tasks is crucial for realizing their cross-domain potential but often relies on sensitive data, raising privacy concerns. Differenti…
cs.CR2025
Guard-GBDT: Efficient Privacy-Preserving Approximated GBDT Training on Vertical Dataset
Anxiao Song, Shujie Cui, Jianli Bai +3
In light of increasing privacy concerns and stringent legal regulations, using secure multiparty computation (MPC) to enable collaborative GBDT model training among multiple data o…
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…