1.3k citations · 1.4k across the 11 of their papers we have counts for
11 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…
McFIL: Model Counting Functionality-Inherent Leakage
Maximilian Zinkus, Yinzhi Cao, Matthew Green
Protecting the confidentiality of private data and using it for useful collaboration have long been at odds. Modern cryptography is bridging this gap through rapid growth in secure…
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…
Practical Blind Membership Inference Attack via Differential Comparisons
Bo Hui, Yuchen Yang, Haolin Yuan +3
Membership inference (MI) attacks affect user privacy by inferring whether given data samples have been used to train a target learning model, e.g., a deep neural network. There ar…
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…
Towards Practical Verification of Machine Learning: The Case of Computer Vision Systems
Kexin Pei, Linjie Zhu, Yinzhi Cao +3
Due to the increasing usage of machine learning (ML) techniques in security- and safety-critical domains, such as autonomous systems and medical diagnosis, ensuring correct behavio…
Deterministic Browser
Yinzhi Cao, Zhanhao Chen, Song Li +1
Timing attacks have been a continuous threat to users' privacy in modern browsers. To mitigate such attacks, existing approaches, such as Tor Browser and Fermata, add jitters to th…
DeepXplore: Automated Whitebox Testing of Deep Learning Systems
Kexin Pei, Yinzhi Cao, Junfeng Yang +1
Deep learning (DL) systems are increasingly deployed in safety- and security-critical domains including self-driving cars and malware detection, where the correctness and predictab…