1.5k citations · 2.9k across the 29 of their papers we have counts for
29 papers
GuardFL: Safeguarding Federated Learning Against Backdoor Attacks through Attributed Client Graph Clustering
Hao Yu, Chuan Ma, Meng Liu +5
Federated Learning (FL) offers collaborative model training without data sharing but is vulnerable to backdoor attacks, where poisoned model weights lead to compromised system inte…
Efficient and Low Overhead Website Fingerprinting Attacks and Defenses based on TCP/IP Traffic
Guodong Huang, Chuan Ma, Ming Ding +4
Website fingerprinting attack is an extensively studied technique used in a web browser to analyze traffic patterns and thus infer confidential information about users. Several web…
Differentially Private Vertical Federated Learning
Thilina Ranbaduge, Ming Ding
A successful machine learning (ML) algorithm often relies on a large amount of high-quality data to train well-performed models. Supervised learning approaches, such as deep learni…
Privacy-preserving Deep Learning based Record Linkage
Thilina Ranbaduge, Dinusha Vatsalan, Ming Ding
Deep learning-based linkage of records across different databases is becoming increasingly useful in data integration and mining applications to discover new insights from multiple…
How Does a Deep Learning Model Architecture Impact Its Privacy? A Comprehensive Study of Privacy Attacks on CNNs and Transformers
Guangsheng Zhang, Bo Liu, Huan Tian +3
As a booming research area in the past decade, deep learning technologies have been driven by big data collected and processed on an unprecedented scale. However, privacy concerns…
Universal Optimality and Robust Utility Bounds for Metric Differential Privacy
Natasha Fernandes, Annabelle McIver, Catuscia Palamidessi +1
We study the privacy-utility trade-off in the context of metric differential privacy. Ghosh et al. introduced the idea of universal optimality to characterise the best mechanism fo…