2 citations · 2 across the 6 of their papers we have counts for
7 papers
LymphNode: A Plug-and-Play Access Control Method for Deep Neural Networks
Hanyu Pei, Shang Liu, Zeyan Liu
Deep Neural Networks (DNNs) are high-value intellectual property (IP), yet deploying them to edge environments exposes them to \textbf{unrestricted oracle access}, rendering them v…
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…
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…
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…
Federated Graph Analytics with Differential Privacy
Shang Liu, Yang Cao, Takao Murakami +5
Collaborative graph analysis across multiple institutions is becoming increasingly popular. Realistic examples include social network analysis across various social platforms, fina…
Federated Graph Condensation with Information Bottleneck Principles
Bo Yan, Sihao He, Cheng Yang +3
Graph condensation (GC), which reduces the size of a large-scale graph by synthesizing a small-scale condensed graph as its substitution, has benefited various graph learning tasks…
CARGO: Crypto-Assisted Differentially Private Triangle Counting without Trusted Servers
Shang Liu, Yang Cao, Takao Murakami +2
Differentially private triangle counting in graphs is essential for analyzing connection patterns and calculating clustering coefficients while protecting sensitive individual info…