3 citations · 6 across the 6 of their papers we have counts for
6 papers
Distributed Backdoor Attacks on Federated Graph Learning and Certified Defenses
Yuxin Yang, Qiang Li, Jinyuan Jia +2
Federated graph learning (FedGL) is an emerging federated learning (FL) framework that extends FL to learn graph data from diverse sources. FL for non-graph data has shown to be vu…
Inf2Guard: An Information-Theoretic Framework for Learning Privacy-Preserving Representations against Inference Attacks
Sayedeh Leila Noorbakhsh, Binghui Zhang, Yuan Hong +1
Machine learning (ML) is vulnerable to inference (e.g., membership inference, property inference, and data reconstruction) attacks that aim to infer the private information of trai…
FLTracer: Accurate Poisoning Attack Provenance in Federated Learning
Xinyu Zhang, Qingyu Liu, Zhongjie Ba +5
Federated Learning (FL) is a promising distributed learning approach that enables multiple clients to collaboratively train a shared global model. However, recent studies show that…
Certified Adversarial Robustness via Anisotropic Randomized Smoothing
Hanbin Hong, Yuan Hong
Randomized smoothing has achieved great success for certified robustness against adversarial perturbations. Given any arbitrary classifier, randomized smoothing can guarantee the c…
UniCR: Universally Approximated Certified Robustness via Randomized Smoothing
Hanbin Hong, Binghui Wang, Yuan Hong
We study certified robustness of machine learning classifiers against adversarial perturbations. In particular, we propose the first universally approximated certified robustness (…
DPOAD: Differentially Private Outsourcing of Anomaly Detection through Iterative Sensitivity Learning
Meisam Mohammady, Han Wang, Lingyu Wang +6
Outsourcing anomaly detection to third-parties can allow data owners to overcome resource constraints (e.g., in lightweight IoT devices), facilitate collaborative analysis (e.g., u…