3 papers
cs.CR2026
PrivCode++: Latent-Conditioned Differentially Private Code Generation for Comprehensive Guarantees
Zheng Liu, Chen Gong, Terry Yue Zhuo +6
Large language models fine-tuned on instruction-code pairs may memorize and subsequently leak sensitive training data. Existing differentially private (DP) code generation methods…
cs.CL2025
EnchTable: Unified Safety Alignment Transfer in Fine-tuned Large Language Models
Jialin Wu, Kecen Li, Zhicong Huang +3
Many machine learning models are fine-tuned from large language models (LLMs) to achieve high performance in specialized domains like code generation, biomedical analysis, and math…
cs.CR2025
Dual-Priv Pruning : Efficient Differential Private Fine-Tuning in Multimodal Large Language Models
Qianshan Wei, Jiaqi Li, Zihan You +9
Differential Privacy (DP) is a widely adopted technique, valued for its effectiveness in protecting the privacy of task-specific datasets, making it a critical tool for large langu…