3 citations · 3 across the 5 of their papers we have counts for
7 papers
Towards Privacy-Preserving LLM Inference via Covariant Obfuscation (Technical Report)
Yu Lin, Qizhi Zhang, Wenqiang Ruan +6
The rapid development of large language models (LLMs) has driven the widespread adoption of cloud-based LLM inference services, while also bringing prominent privacy risks associat…
CryptoMoE: Privacy-Preserving and Scalable Mixture of Experts Inference via Balanced Expert Routing
Yifan Zhou, Tianshi Xu, Jue Hong +2
Private large language model (LLM) inference based on cryptographic primitives offers a promising path towards privacy-preserving deep learning. However, existing frameworks only s…
Black-box Optimization of LLM Outputs by Asking for Directions
Jie Zhang, Meng Ding, Yang Liu +2
We present a novel approach for attacking black-box large language models (LLMs) by exploiting their ability to express confidence in natural language. Existing black-box attacks r…
PubSub-VFL: Towards Efficient Two-Party Split Learning in Heterogeneous Environments via Publisher/Subscriber Architecture
Yi Liu, Yang Liu, Leqian Zheng +5
With the rapid advancement of the digital economy, data collaboration between organizations has become a well-established business model, driving the growth of various industries.…
AgentArmor: Enforcing Program Analysis on Agent Runtime Trace to Defend Against Prompt Injection
Peiran Wang, Yang Liu, Yunfei Lu +6
Large Language Model (LLM) agents offer a powerful new paradigm for solving various problems by combining natural language reasoning with the execution of external tools. However,…
What are Models Thinking about? Understanding Large Language Model Hallucinations "Psychology" through Model Inner State Analysis
Peiran Wang, Yang Liu, Yunfei Lu +2
Large language model (LLM) systems suffer from the models' unstable ability to generate valid and factual content, resulting in hallucination generation. Current hallucination dete…
A Split-and-Privatize Framework for Large Language Model Fine-Tuning
Xicong Shen, Yang Liu, Huiqi Liu +6
Fine-tuning is a prominent technique to adapt a pre-trained language model to downstream scenarios. In parameter-efficient fine-tuning, only a small subset of modules are trained o…