95 citations · 298 across the 14 of their papers we have counts for
12 papers · 1 filter
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.…
IPGuard: Protecting Intellectual Property of Deep Neural Networks via Fingerprinting the Classification Boundary
Xiaoyu Cao, Jinyuan Jia, Neil Zhenqiang Gong
A deep neural network (DNN) classifier represents a model owner's intellectual property as training a DNN classifier often requires lots of resource. Watermarking was recently prop…
Defending against Machine Learning based Inference Attacks via Adversarial Examples: Opportunities and Challenges
Jinyuan Jia, Neil Zhenqiang Gong
As machine learning (ML) becomes more and more powerful and easily accessible, attackers increasingly leverage ML to perform automated large-scale inference attacks in various doma…
MemGuard: Defending against Black-Box Membership Inference Attacks via Adversarial Examples
Jinyuan Jia, Ahmed Salem, Michael Backes +2
In a membership inference attack, an attacker aims to infer whether a data sample is in a target classifier's training dataset or not. Specifically, given a black-box access to the…
Calibrate: Frequency Estimation and Heavy Hitter Identification with Local Differential Privacy via Incorporating Prior Knowledge
Jinyuan Jia, Neil Zhenqiang Gong
Estimating frequencies of certain items among a population is a basic step in data analytics, which enables more advanced data analytics (e.g., heavy hitter identification, frequen…
Graph-based Security and Privacy Analytics via Collective Classification with Joint Weight Learning and Propagation
Binghui Wang, Jinyuan Jia, Neil Zhenqiang Gong
Many security and privacy problems can be modeled as a graph classification problem, where nodes in the graph are classified by collective classification simultaneously. State-of-t…
AttriGuard: A Practical Defense Against Attribute Inference Attacks via Adversarial Machine Learning
Jinyuan Jia, Neil Zhenqiang Gong
Users in various web and mobile applications are vulnerable to attribute inference attacks, in which an attacker leverages a machine learning classifier to infer a target user's pr…
Structure-based Sybil Detection in Social Networks via Local Rule-based Propagation
Binghui Wang, Jinyuan Jia, Le Zhang +1
Sybil detection in social networks is a basic security research problem. Structure-based methods have been shown to be promising at detecting Sybils. Existing structure-based metho…