8 papers
Protecting Floating-Point Computation for DNN Binaries with MBA Obfuscation
Yikun Hu, Zichen Zhao, Peixiang Qin +4
This submission was made prematurely and has been withdrawn by the authors for substantial revision before further dissemination.
On the (In-)Security of the Shuffling Defense in the Transformer Secure Inference
Zhengyi Li, Yakai Wang, Kang Yang +6
For Transformer models, cryptographically secure inference ensures that the client learns only the final output, while the server learns nothing about the client's input. However,…
Towards Explainable Privacy Preservation in Federated Learning via Shapley Value-Guided Noise Injection
Yunbo Li, Jiaping Gui, Yue Wu
This paper proposes FedSVA, an explainable differential privacy (DP) mechanism for federated learning (FL) that dynamically calibrates noise injection based on the privacy contribu…
From Secure Agentic AI to Secure Agentic Web: Challenges, Threats, and Future Directions
Zhihang Deng, Jiaping Gui, Weinan Zhang
Large Language Models (LLMs) are increasingly deployed as agentic systems that plan, memorize, and act in open-world environments. This shift brings new security problems: failures…
Local Layer-wise Differential Privacy in Federated Learning
Yunbo Li, Jiaping Gui, Fanchao Meng +1
Federated Learning (FL) enables collaborative model training without direct data sharing, yet it remains vulnerable to privacy attacks such as model inversion and membership infere…
FedQS: Optimizing Gradient and Model Aggregation for Semi-Asynchronous Federated Learning
Yunbo Li, Jiaping Gui, Zhihang Deng +2
Federated learning (FL) enables collaborative model training across multiple parties without sharing raw data, with semi-asynchronous FL (SAFL) emerging as a balanced approach betw…