USENIX Security

2024

• Defending Against Data Reconstruction Attacks in Federated Learning: An Information Theory Approach.

• Lotto: Secure Participant Selection against Adversarial Servers in Federated Learning.

• FAMOS: Robust Privacy-Preserving Authentication on Payment Apps via Federated Multi-Modal Contrastive Learning.

• Efficient Privacy Auditing in Federated Learning.

• Lurking in the shadows: Unveiling Stealthy Backdoor Attacks against Personalized Federated Learning.

• ACE: A Model Poisoning Attack on Contribution Evaluation Methods in Federated Learning.

• BackdoorIndicator: Leveraging OOD Data for Proactive Backdoor Detection in Federated Learning.

2023

• Gradient Obfuscation Gives a False Sense of Security in Federated Learning.

• Every Vote Counts: Ranking-Based Training of Federated Learning to Resist Poisoning Attacks.

• PrivateFL: Accurate, Differentially Private Federated Learning via Personalized Data Transformation.

• VILLAIN: Backdoor Attacks Against Vertical Split Learning.

2022

• SIMC: ML Inference Secure Against Malicious Clients at Semi-Honest Cost.

• Efficient Differentially Private Secure Aggregation for Federated Learning via Hardness of Learning with Errors.

• Label Inference Attacks Against Vertical Federated Learning.

• FLAME: Taming Backdoors in Federated Learning.

2020

• Local Model Poisoning Attacks to Byzantine-Robust Federated Learning.