6 citations · 20 across the 11 of their papers we have counts for
13 papers
Guided Diffusion for Trustworthy Synthetic Text Generation
Qi Pang, Hanshen Xiao, Andre Manoel +4
The rapid evolution of Large Language Models is fueled by massive datasets, yet this reliance poses severe privacy risks, as models can inadvertently memorize and even directly out…
Codetta: High-Capacity, Keyless, and Undetectable Multi-Agent Collusion
Qi Pang, Virginia Smith, Wenting Zheng
Multi-agent systems built on large language models (LLMs) are increasingly deployed in high-stakes settings such as finance, healthcare, and software engineering, where agents coor…
BadMoE: Backdooring Mixture-of-Experts LLMs via Optimizing Routing Triggers and Infecting Dormant Experts
Qingyue Wang, Qi Pang, Xixun Lin +2
Mixture-of-Experts (MoE) have emerged as a powerful architecture for large language models (LLMs), enabling efficient scaling of model capacity while maintaining manageable computa…
Unveiling Hidden DNN Defects with Decision-Based Metamorphic Testing
Yuanyuan Yuan, Qi Pang, Shuai Wang
Contemporary DNN testing works are frequently conducted using metamorphic testing (MT). In general, de facto MT frameworks mutate DNN input images using semantics-preserving mutati…
Byzantine-Robust Federated Learning with Optimal Statistical Rates and Privacy Guarantees
Banghua Zhu, Lun Wang, Qi Pang +4
We propose Byzantine-robust federated learning protocols with nearly optimal statistical rates. In contrast to prior work, our proposed protocols improve the dimension dependence a…
ADI: Adversarial Dominating Inputs in Vertical Federated Learning Systems
Qi Pang, Yuanyuan Yuan, Shuai Wang +1
Vertical federated learning (VFL) system has recently become prominent as a concept to process data distributed across many individual sources without the need to centralize it. Mu…
Automated Side Channel Analysis of Media Software with Manifold Learning
Yuanyuan Yuan, Qi Pang, Shuai Wang
The prosperous development of cloud computing and machine learning as a service has led to the widespread use of media software to process confidential media data. This paper explo…
MDPFuzz: Testing Models Solving Markov Decision Processes
Qi Pang, Yuanyuan Yuan, Shuai Wang
The Markov decision process (MDP) provides a mathematical framework for modeling sequential decision-making problems, many of which are crucial to security and safety, such as auto…
Provably Valid and Diverse Mutations of Real-World Media Data for DNN Testing
Yuanyuan Yuan, Qi Pang, Shuai Wang
Deep neural networks (DNNs) often accept high-dimensional media data (e.g., photos, text, and audio) and understand their perceptual content (e.g., a cat). To test DNNs, diverse in…
Revisiting Neuron Coverage for DNN Testing: A Layer-Wise and Distribution-Aware Criterion
Yuanyuan Yuan, Qi Pang, Shuai Wang
Various deep neural network (DNN) coverage criteria have been proposed to assess DNN test inputs and steer input mutations. The coverage is characterized via neurons having certain…
FED-: Privacy Preserving Federated Correlation Test
Lun Wang, Qi Pang, Shuai Wang +1
In this paper, we propose the first secure federated -test protocol Fed-. To minimize both the privacy leakage and the communication cost, we recast -test to the sec…
Towards Bidirectional Protection in Federated Learning
Lun Wang, Qi Pang, Shuai Wang +1
Prior efforts in enhancing federated learning (FL) security fall into two categories. At one end of the spectrum, some work uses secure aggregation techniques to hide the individua…
Towards practical differentially private causal graph discovery
Lun Wang, Qi Pang, Dawn Song
Causal graph discovery refers to the process of discovering causal relation graphs from purely observational data. Like other statistical data, a causal graph might leak sensitive…