2 papers
cs.CR2026
PrivTune: Efficient and Privacy-Preserving Fine-Tuning of Large Language Models via Device-Cloud Collaboration
Yi Liu, Weixiang Han, Chengjun Cai +2
With the rise of large language models, service providers offer language models as a service, enabling users to fine-tune customized models via uploaded private datasets. However,…
cs.CR2025
Training with Differential Privacy: A Gradient-Preserving Noise Reduction Approach with Provable Security
Haodi Wang, Tangyu Jiang, Yu Guo +3
Deep learning models have been extensively adopted in various regions due to their ability to represent hierarchical features, which highly rely on the training set and procedures.…