7 papers
Trust-But-Verify: Poisoning-Resilient Locally Private Graph Learning Protocols
Longzhu He, Li Sun, Hao Peng +3
Built upon local differential privacy (LDP), locally private graph learning protocols have emerged as an important paradigm for decentralized graph learning, balancing privacy prot…
Auditing Privacy Risks in LLM-Enhanced Graph Neural Networks
Longzhu He, Zelang Wen, Chaozhuo Li +1
Large language models (LLMs) have recently advanced graph neural networks (GNNs) by enriching node representations with semantic information, giving rise to LLM-enhanced GNNs that…
Towards Personalized Differentially Private Learning for Decentralized Local Graphs
Longzhu He, Peng Tang, Chaozhuo Li +5
Graph-structured data is increasingly generated and stored in decentralized environments, such as social platforms, mobile applications, and edge networks, where users maintain con…
Conflict-Aware Multi-Agent Reasoning via Signed Graph Modeling
Longgang He, Longzhu He, Daojing He +2
LLM-based multi-agent systems (MAS) seek to improve reasoning by combining diverse perspectives from multiple agents. However, aggregation may overlook supportive, contradictory, a…
Learning to Edit Knowledge via Instruction-based Chain-of-Thought Prompting
Jinhu Fu, Yan Bai, Longzhu He +4
Large language models (LLMs) can effectively handle outdated information through knowledge editing. However, current approaches face two key limitations: (I) Poor generalization: M…
Devil's Hand: Data Poisoning Attacks to Locally Private Graph Learning Protocols
Longzhu He, Chaozhuo Li, Peng Tang +3
Graph neural networks (GNNs) have achieved significant success in graph representation learning and have been applied to various domains. However, many real-world graphs contain se…
Alignment-Enhanced Decoding:Defending via Token-Level Adaptive Refining of Probability Distributions
Quan Liu, Zhenhong Zhou, Longzhu He +3
Large language models are susceptible to jailbreak attacks, which can result in the generation of harmful content. While prior defenses mitigate these risks by perturbing or inspec…