3 papers
cs.CR2025
Can Differentially Private Fine-tuning LLMs Protect Against Privacy Attacks?
Hao Du, Shang Liu, Yang Cao
Fine-tuning large language models (LLMs) has become an essential strategy for adapting them to specialized tasks; however, this process introduces significant privacy challenges, a…
cs.AI2025
Privacy in Fine-tuning Large Language Models: Attacks, Defenses, and Future Directions
Hao Du, Shang Liu, Lele Zheng +3
Fine-tuning has emerged as a critical process in leveraging Large Language Models (LLMs) for specific downstream tasks, enabling these models to achieve state-of-the-art performanc…
cs.DB2024
PGB: Benchmarking Differentially Private Synthetic Graph Generation Algorithms
Shang Liu, Hao Du, Yang Cao +3
Differentially private graph analysis is a powerful tool for deriving insights from diverse graph data while protecting individual information. Designing private analytic algorithm…