225 citations · 805 across the 24 of their papers we have counts for
24 papers
Gradient-Leakage Resilient Federated Learning
Wenqi Wei, Ling Liu, Yanzhao Wu +2
Federated learning(FL) is an emerging distributed learning paradigm with default client privacy because clients can keep sensitive data on their devices and only share local traini…
Security and Privacy for Healthcare Blockchains
Rui Zhang, Rui Xue, Ling Liu
Healthcare blockchains provide an innovative way to store healthcare information, execute healthcare transactions, and build trust for healthcare data sharing and data integration…
De-Pois: An Attack-Agnostic Defense against Data Poisoning Attacks
Jian Chen, Xuxin Zhang, Rui Zhang +2
Machine learning techniques have been widely applied to various applications. However, they are potentially vulnerable to data poisoning attacks, where sophisticated attackers can…
Robust Deep Learning Ensemble against Deception
Wenqi Wei, Ling Liu
Deep neural network (DNN) models are known to be vulnerable to maliciously crafted adversarial examples and to out-of-distribution inputs drawn sufficiently far away from the train…
Utility-Optimized Synthesis of Differentially Private Location Traces
Mehmet Emre Gursoy, Vivekanand Rajasekar, Ling Liu
Differentially private location trace synthesis (DPLTS) has recently emerged as a solution to protect mobile users' privacy while enabling the analysis and sharing of their locatio…
Data Poisoning Attacks Against Federated Learning Systems
Vale Tolpegin, Stacey Truex, Mehmet Emre Gursoy +1
Federated learning (FL) is an emerging paradigm for distributed training of large-scale deep neural networks in which participants' data remains on their own devices with only mode…