38 citations · 46 across the 4 of their papers we have counts for
6 papers · 1 filter
Semi-Supervised Node Classification on Graphs: Markov Random Fields vs. Graph Neural Networks
Binghui Wang, Jinyuan Jia, Neil Zhenqiang Gong
Semi-supervised node classification on graph-structured data has many applications such as fraud detection, fake account and review detection, user's private attribute inference in…
Robust and Verifiable Information Embedding Attacks to Deep Neural Networks via Error-Correcting Codes
Jinyuan Jia, Binghui Wang, Neil Zhenqiang Gong
In the era of deep learning, a user often leverages a third-party machine learning tool to train a deep neural network (DNN) classifier and then deploys the classifier as an end-us…
On the Intrinsic Differential Privacy of Bagging
Hongbin Liu, Jinyuan Jia, Neil Zhenqiang Gong
Differentially private machine learning trains models while protecting privacy of the sensitive training data. The key to obtain differentially private models is to introduce noise…
Backdoor Attacks to Graph Neural Networks
Zaixi Zhang, Jinyuan Jia, Binghui Wang +1
In this work, we propose the first backdoor attack to graph neural networks (GNN). Specifically, we propose a \emph{subgraph based backdoor attack} to GNN for graph classification.…
Stealing Links from Graph Neural Networks
Xinlei He, Jinyuan Jia, Michael Backes +2
Graph data, such as chemical networks and social networks, may be deemed confidential/private because the data owner often spends lots of resources collecting the data or the data…
On Certifying Robustness against Backdoor Attacks via Randomized Smoothing
Binghui Wang, Xiaoyu Cao, Jinyuan jia +1
Backdoor attack is a severe security threat to deep neural networks (DNNs). We envision that, like adversarial examples, there will be a cat-and-mouse game for backdoor attacks, i.…