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cs.CR2024
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.…
cs.LG2024
LMEraser: Large Model Unlearning through Adaptive Prompt Tuning
Jie Xu, Zihan Wu, Cong Wang +1
To address the growing demand for privacy protection in machine learning, we propose a novel and efficient machine unlearning approach for \textbf{L}arge \textbf{M}odels, called \t…