8 citations · 42 across the 13 of their papers we have counts for
13 papers
3DHacker: Spectrum-based Decision Boundary Generation for Hard-label 3D Point Cloud Attack
Yunbo Tao, Daizong Liu, Pan Zhou +3
With the maturity of depth sensors, the vulnerability of 3D point cloud models has received increasing attention in various applications such as autonomous driving and robot naviga…
UOR: Universal Backdoor Attacks on Pre-trained Language Models
Wei Du, Peixuan Li, Boqun Li +2
Backdoors implanted in pre-trained language models (PLMs) can be transferred to various downstream tasks, which exposes a severe security threat. However, most existing backdoor at…
A Universal Identity Backdoor Attack against Speaker Verification based on Siamese Network
Haodong Zhao, Wei Du, Junjie Guo +1
Speaker verification has been widely used in many authentication scenarios. However, training models for speaker verification requires large amounts of data and computing power, so…
FedPrompt: Communication-Efficient and Privacy Preserving Prompt Tuning in Federated Learning
Haodong Zhao, Wei Du, Fangqi Li +2
Federated learning (FL) has enabled global model training on decentralized data in a privacy-preserving way by aggregating model updates. However, for many natural language process…
Poisoning Attacks on Fair Machine Learning
Minh-Hao Van, Wei Du, Xintao Wu +1
Both fair machine learning and adversarial learning have been extensively studied. However, attacking fair machine learning models has received less attention. In this paper, we pr…
Fair Regression under Sample Selection Bias
Wei Du, Xintao Wu, Hanghang Tong
Recent research on fair regression focused on developing new fairness notions and approximation methods as target variables and even the sensitive attribute are continuous in the r…
Robust Fairness-aware Learning Under Sample Selection Bias
Wei Du, Xintao Wu
The underlying assumption of many machine learning algorithms is that the training data and test data are drawn from the same distributions. However, the assumption is often violat…
Fairness-aware Agnostic Federated Learning
Wei Du, Depeng Xu, Xintao Wu +1
Federated learning is an emerging framework that builds centralized machine learning models with training data distributed across multiple devices. Most of the previous works about…
PoliteCamera: Respecting Strangers' Privacy in Mobile Photographing
Ang Li, Wei Du, Qinghua Li
Camera is a standard on-board sensor of modern mobile phones. It makes photo taking popular due to its convenience and high resolution. However, when users take a photo of a scener…
Removing Disparate Impact of Differentially Private Stochastic Gradient Descent on Model Accuracy
Depeng Xu, Wei Du, Xintao Wu
When we enforce differential privacy in machine learning, the utility-privacy trade-off is different w.r.t. each group. Gradient clipping and random noise addition disproportionate…
Transfer Heterogeneous Knowledge Among Peer-to-Peer Teammates: A Model Distillation Approach
Zeyue Xue, Shuang Luo, Chao Wu +3
Peer-to-peer knowledge transfer in distributed environments has emerged as a promising method since it could accelerate learning and improve team-wide performance without relying o…
Privacy-Preserving Multiparty Learning For Logistic Regression
Wei Du, Ang Li, Qinghua Li
In recent years, machine learning techniques are widely used in numerous applications, such as weather forecast, financial data analysis, spam filtering, and medical prediction. In…
Privacy-Preserving Outsourcing of Large-Scale Nonlinear Programming to the Cloud
Ang Li, Wei Du, Qinghua Li
The increasing massive data generated by various sources has given birth to big data analytics. Solving large-scale nonlinear programming problems (NLPs) is one important big data…