20 citations · 33 across the 12 of their papers we have counts for
12 papers
FAgent: Financial Fusion of Agentic Intelligence for Multimodal Trading
Changshuo Liu, Yanzheng Jin, Shangfeng Cai +3
With increasingly diverse and heterogeneous information sources, effectively leveraging multimodal data is becoming pivotal for high-quality financial trading. Although recent adva…
Differentially Private Contrastive Learning via Bounding Group-level Contribution
Kecen Li, Chen Gong, Zinan Lin +2
Differentially private (DP) contrastive learning aims to learn general-purpose representations from sensitive data, alleviating the privacy leakage concerns of organizations deploy…
NeurStore: Efficient In-database Deep Learning Model Management System
Siqi Xiang, Sheng Wang, Xiaokui Xiao +3
With the prevalence of in-database AI-powered analytics, there is an increasing demand for database systems to efficiently manage the ever-expanding number and size of deep learnin…
Prompt Inference Attack on Distributed Large Language Model Inference Frameworks
Xinjian Luo, Ting Yu, Xiaokui Xiao
The inference process of modern large language models (LLMs) demands prohibitive computational resources, rendering them infeasible for deployment on consumer-grade devices. To add…
Calibrating Noise for Group Privacy in Subsampled Mechanisms
Yangfan Jiang, Xinjian Luo, Yin Yang +1
Given a group size m and a sensitive dataset D, group privacy (GP) releases information about D with the guarantee that the adversary cannot infer with high confidence whether the…
GCON: Differentially Private Graph Convolutional Network via Objective Perturbation
Jianxin Wei, Yizheng Zhu, Xiaokui Xiao +4
Graph Convolutional Networks (GCNs) are a popular machine learning model with a wide range of applications in graph analytics, including healthcare, transportation, and finance. Ho…
Proof of Quality: A Costless Paradigm for Trustless Generative AI Model Inference on Blockchains
Zhenjie Zhang, Yuyang Rao, Hao Xiao +2
Generative AI models, such as GPT-4 and Stable Diffusion, have demonstrated powerful and disruptive capabilities in natural language and image tasks. However, deploying these model…
NeurDB: An AI-powered Autonomous Data System
Beng Chin Ooi, Shaofeng Cai, Gang Chen +9
In the wake of rapid advancements in artificial intelligence (AI), we stand on the brink of a transformative leap in data systems. The imminent fusion of AI and DB (AIxDB) promises…
Exploring Privacy and Fairness Risks in Sharing Diffusion Models: An Adversarial Perspective
Xinjian Luo, Yangfan Jiang, Fei Wei +3
Diffusion models have recently gained significant attention in both academia and industry due to their impressive generative performance in terms of both sampling quality and distr…
Secure and Verifiable Data Collaboration with Low-Cost Zero-Knowledge Proofs
Yizheng Zhu, Yuncheng Wu, Zhaojing Luo +2
Organizations are increasingly recognizing the value of data collaboration for data analytics purposes. Yet, stringent data protection laws prohibit the direct exchange of raw data…
Passive Inference Attacks on Split Learning via Adversarial Regularization
Xiaochen Zhu, Xinjian Luo, Yuncheng Wu +3
Split Learning (SL) has emerged as a practical and efficient alternative to traditional federated learning. While previous attempts to attack SL have often relied on overly strong…
Blink: Link Local Differential Privacy in Graph Neural Networks via Bayesian Estimation
Xiaochen Zhu, Vincent Y. F. Tan, Xiaokui Xiao
Graph neural networks (GNNs) have gained an increasing amount of popularity due to their superior capability in learning node embeddings for various graph inference tasks, but trai…