7 citations · 7 across the 6 of their papers we have counts for
6 papers
Embedded Graph Flows for Categorical Graph Generation
Ethan Ma, Zihan Wang, Chris Siu Yeung Chow +5
Generating categorical graphs requires choosing node and edge types that form a coherent structure without depending on node order. Many graph generators encode categories as fixed…
Shadow Queries for Private Retrieval in Vector Databases
Xinguo Feng, Zhongkui Ma, Zihan Wang +4
Large language models (LLMs) increasingly rely on information retrieval (IR) systems, such as Retrieval-Augmented Generation (RAG), to incorporate domain-specific knowledge without…
AI Model Modulation with Logits Redistribution
Zihan Wang, Zhongkui Ma, Xinguo Feng +5
Large-scale models are typically adapted to meet the diverse requirements of model owners and users. However, maintaining multiple specialized versions of the model is inefficient.…
Mitigating Gradient Inversion Risks in Language Models via Token Obfuscation
Xinguo Feng, Zhongkui Ma, Zihan Wang +2
Training and fine-tuning large-scale language models largely benefit from collaborative learning, but the approach has been proven vulnerable to gradient inversion attacks (GIAs),…
Re-Key-Free, Risky-Free: Adaptable Model Usage Control
Zihan Wang, Zhongkui Ma, Xinguo Feng +6
Deep neural networks (DNNs) have become valuable intellectual property of model owners, due to the substantial resources required for their development. To protect these assets in…
Uncovering Gradient Inversion Risks in Practical Language Model Training
Xinguo Feng, Zhongkui Ma, Zihan Wang +4
The gradient inversion attack has been demonstrated as a significant privacy threat to federated learning (FL), particularly in continuous domains such as vision models. In contras…