NDSS

2025

• Passive Inference Attacks on Split Learning via Adversarial Regularization.

• CENSOR: Defense Against Gradient Inversion via Orthogonal Subspace Bayesian Sampling.

• RAIFLE: Reconstruction Attacks on Interaction-based Federated Learning with Adversarial Data Manipulation.

• SafeSplit: A Novel Defense Against Client-Side Backdoor Attacks in Split Learning.

• URVFL: Undetectable Data Reconstruction Attack on Vertical Federated Learning.

2024

• FP-Fed: Privacy-Preserving Federated Detection of Browser Fingerprinting.

• CrowdGuard: Federated Backdoor Detection in Federated Learning.

• Automatic Adversarial Adaption for Stealthy Poisoning Attacks in Federated Learning.

• FreqFed: A Frequency Analysis-Based Approach for Mitigating Poisoning Attacks in Federated Learning.

2023

• Securing Federated Sensitive Topic Classification against Poisoning Attacks.

• PPA: Preference Profiling Attack Against Federated Learning.

2022

• Local and Central Differential Privacy for Robustness and Privacy in Federated Learning.

• DeepSight: Mitigating Backdoor Attacks in Federated Learning Through Deep Model Inspection.

• FedCRI: Federated Mobile Cyber-Risk Intelligence.

• Interpretable Federated Transformer Log Learning for Cloud Threat Forensics.

2021

• POSEIDON: Privacy-Preserving Federated Neural Network Learning.

• FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping.

• Manipulating the Byzantine: Optimizing Model Poisoning Attacks and Defenses for Federated Learning.