139 citations · 302 across the 13 of their papers we have counts for
10 papers
A Novel Attribute Reconstruction Attack in Federated Learning
Lingjuan Lyu, Chen Chen
Federated learning (FL) emerged as a promising learning paradigm to enable a multitude of participants to construct a joint ML model without exposing their private training data. E…
Robust Training Using Natural Transformation
Shuo Wang, Lingjuan Lyu, Surya Nepal +3
Previous robustness approaches for deep learning models such as data augmentation techniques via data transformation or adversarial training cannot capture real-world variations th…
Model Extraction and Adversarial Transferability, Your BERT is Vulnerable!
Xuanli He, Lingjuan Lyu, Qiongkai Xu +1
Natural language processing (NLP) tasks, ranging from text classification to text generation, have been revolutionised by the pre-trained language models, such as BERT. This allows…
Neural Attention Distillation: Erasing Backdoor Triggers from Deep Neural Networks
Yige Li, Xixiang Lyu, Nodens Koren +3
Deep neural networks (DNNs) are known vulnerable to backdoor attacks, a training time attack that injects a trigger pattern into a small proportion of training data so as to contro…
A Fast and Scalable Authentication Scheme in IoT for Smart Living
Jianhua Li, Jiong Jin, Lingjuan Lyu +4
Numerous resource-limited smart objects (SOs) such as sensors and actuators have been widely deployed in smart environments, opening new attack surfaces to intruders. The severe se…
Differentially Private Representation for NLP: Formal Guarantee and An Empirical Study on Privacy and Fairness
Lingjuan Lyu, Xuanli He, Yitong Li
It has been demonstrated that hidden representation learned by a deep model can encode private information of the input, hence can be exploited to recover such information with rea…