20 citations · 33 across the 12 of their papers we have counts for
5 papers · 1 filter
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…