Xinlei He

Model Stealing Attacks Against Inductive Graph Neural Networks

2022 · 2022 IEEE Symposium on Security and Privacy (SP)

Many real-world data come in the form of graphs. Graph neural networks (GNNs), a new family of machine learning (ML) models, have been proposed to fully leverage graph data to build powerful applications. In particular, …

CL-Attack: Textual Backdoor Attacks via Cross-Lingual Triggers

2024 · arXiv (Cornell University)

Backdoor attacks significantly compromise the security of large language models by triggering them to output specific and controlled content. Currently, triggers for textual backdoor attacks fall into two categories: fixed-token triggers and sentence-pattern triggers. However, …

Verifiable and Lightweight Multi-Round Secure Federated Learning

2025 · IEEE Transactions on Dependable and Secure Computing

Federated learning (FL) is a paradigm that ensures the confidentiality and accessibility of data without requiring the collection of private data from multiple sources. It acquires an aggregation model by integrating various local models from …