3 citations · 5 across the 5 of their papers we have counts for
7 papers
CDN Tsunami: Exploiting HTTP/3-HTTP/1.1 Conversion for DoS Attacks
Ziyu Lin, Tianlong Su, Yingjie Lin +4
Content Delivery Networks (CDNs) provide high availability, accelerate content delivery for their host websites, but are also vulnerable to different types of Denial-of-Service (Do…
Addressing Heterogeneity in Federated Learning via Distributional Transformation
Haolin Yuan, Bo Hui, Yuchen Yang +3
Federated learning (FL) allows multiple clients to collaboratively train a deep learning model. One major challenge of FL is when data distribution is heterogeneous, i.e., differs…
GraphTrack: A Graph-based Cross-Device Tracking Framework
Binghui Wang, Tianchen Zhou, Song Li +2
Cross-device tracking has drawn growing attention from both commercial companies and the general public because of its privacy implications and applications for user profiling, per…
EdgeMixup: Improving Fairness for Skin Disease Classification and Segmentation
Haolin Yuan, Armin Hadzic, William Paul +5
Skin lesions can be an early indicator of a wide range of infectious and other diseases. The use of deep learning (DL) models to diagnose skin lesions has great potential in assist…
Defending Medical Image Diagnostics against Privacy Attacks using Generative Methods
William Paul, Yinzhi Cao, Miaomiao Zhang +1
Machine learning (ML) models used in medical imaging diagnostics can be vulnerable to a variety of privacy attacks, including membership inference attacks, that lead to violations…
PatchAttack: A Black-box Texture-based Attack with Reinforcement Learning
Chenglin Yang, Adam Kortylewski, Cihang Xie +2
Patch-based attacks introduce a perceptible but localized change to the input that induces misclassification. A limitation of current patch-based black-box attacks is that they per…