211 citations · 1.4k across the 61 of their papers we have counts for
5 papers · 1 filter
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…
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…
A Fusion-Denoising Attack on InstaHide with Data Augmentation
Xinjian Luo, Xiaokui Xiao, Yuncheng Wu +2
InstaHide is a state-of-the-art mechanism for protecting private training images, by mixing multiple private images and modifying them such that their visual features are indisting…
Privacy Preserving Vertical Federated Learning for Tree-based Models
Yuncheng Wu, Shaofeng Cai, Xiaokui Xiao +2
Federated learning (FL) is an emerging paradigm that enables multiple organizations to jointly train a model without revealing their private data to each other. This paper studies…