139 citations · 492 across the 39 of their papers we have counts for
15 papers · 1 filter
The Perils of Learning From Unlabeled Data: Backdoor Attacks on Semi-supervised Learning
Virat Shejwalkar, Lingjuan Lyu, Amir Houmansadr
Semi-supervised machine learning (SSL) is gaining popularity as it reduces the cost of training ML models. It does so by using very small amounts of (expensive, well-inspected) lab…
Extracted BERT Model Leaks More Information than You Think!
Xuanli He, Chen Chen, Lingjuan Lyu +1
The collection and availability of big data, combined with advances in pre-trained models (e.g. BERT), have revolutionized the predictive performance of natural language processing…
How to Sift Out a Clean Data Subset in the Presence of Data Poisoning?
Yi Zeng, Minzhou Pan, Himanshu Jahagirdar +3
Given the volume of data needed to train modern machine learning models, external suppliers are increasingly used. However, incorporating external data poses data poisoning risks,…
Narcissus: A Practical Clean-Label Backdoor Attack with Limited Information
Yi Zeng, Minzhou Pan, Hoang Anh Just +3
Backdoor attacks insert malicious data into a training set so that, during inference time, it misclassifies inputs that have been patched with a backdoor trigger as the malware spe…
Threats to Pre-trained Language Models: Survey and Taxonomy
Shangwei Guo, Chunlong Xie, Jiwei Li +2
Pre-trained language models (PTLMs) have achieved great success and remarkable performance over a wide range of natural language processing (NLP) tasks. However, there are also gro…
Protecting Intellectual Property of Language Generation APIs with Lexical Watermark
Xuanli He, Qiongkai Xu, Lingjuan Lyu +2
Nowadays, due to the breakthrough in natural language generation (NLG), including machine translation, document summarization, image captioning, etc NLG models have been encapsulat…
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…
Student Surpasses Teacher: Imitation Attack for Black-Box NLP APIs
Qiongkai Xu, Xuanli He, Lingjuan Lyu +2
Machine-learning-as-a-service (MLaaS) has attracted millions of users to their splendid large-scale models. Although published as black-box APIs, the valuable models behind these s…
Privacy and Robustness in Federated Learning: Attacks and Defenses
Lingjuan Lyu, Han Yu, Xingjun Ma +5
As data are increasingly being stored in different silos and societies becoming more aware of data privacy issues, the traditional centralized training of artificial intelligence (…
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…
Federated Model Distillation with Noise-Free Differential Privacy
Lichao Sun, Lingjuan Lyu
Conventional federated learning directly averages model weights, which is only possible for collaboration between models with homogeneous architectures. Sharing prediction instead…
Local Differential Privacy and Its Applications: A Comprehensive Survey
Mengmeng Yang, Lingjuan Lyu, Jun Zhao +2
With the fast development of Information Technology, a tremendous amount of data have been generated and collected for research and analysis purposes. As an increasing number of us…
How to Democratise and Protect AI: Fair and Differentially Private Decentralised Deep Learning
Lingjuan Lyu, Yitong Li, Karthik Nandakumar +2
This paper firstly considers the research problem of fairness in collaborative deep learning, while ensuring privacy. A novel reputation system is proposed through digital tokens a…
Cloud-based Privacy-Preserving Collaborative Consumption for Sharing Economy
Lingjuan Lyu, Sid Chi-Kin Chau, Nan Wang +1
Cloud computing has been a dominant paradigm for a variety of information processing platforms, particularly for enabling various popular applications of sharing economy. However,…
Towards Fair and Privacy-Preserving Federated Deep Models
Lingjuan Lyu, Jiangshan Yu, Karthik Nandakumar +5
The current standalone deep learning framework tends to result in overfitting and low utility. This problem can be addressed by either a centralized framework that deploys a centra…