2 citations · 2 across the 4 of their papers we have counts for
9 papers
Minim: Privacy-Aware Minimal View for Agents via Trusted Local Sanitization
Hexuan Yu, Chaoyu Zhang, Heng Jin +4
Modern LLM-powered autonomous agents increasingly rely on rich user interface (UI) state observations to achieve reliable action grounding in complex digital environments. However,…
BoBa: Boosting Backdoor Detection through Data Distribution Inference in Federated Learning
Zhengyuan Jiang, Xingyu Lyu, Shanghao Shi +5
Federated learning, while being a promising approach for collaborative model training, is susceptible to backdoor attacks due to its decentralized nature. Backdoor attacks have sho…
Trusting What You Cannot See: Auditable Fine-Tuning and Inference for Proprietary AI
Heng Jin, Chaoyu Zhang, Hexuan Yu +4
Cloud-based infrastructure has become the dominant platform for deploying large models, particularly large language models (LLMs). Fine-tuning and inference are increasingly delega…
IU-GUARD: Privacy-Preserving Spectrum Coordination for Incumbent Users under Dynamic Spectrum Sharing
Shaoyu Li, Hexuan Yu, Shanghao Shi +4
With the growing demand for wireless spectrum, dynamic spectrum sharing (DSS) frameworks such as the Citizens Broadband Radio Service (CBRS) have emerged as practical solutions to…
Enabling Trustworthy Federated Learning via Remote Attestation for Mitigating Byzantine Threats
Chaoyu Zhang, Heng Jin, Shanghao Shi +4
Federated Learning (FL) has gained significant attention for its privacy-preserving capabilities, enabling distributed devices to collaboratively train a global model without shari…
StarCast: A Secure and Spectrum-Efficient Group Communication Scheme for LEO Satellite Networks
Chaoyu Zhang, Hexuan Yu, Shanghao Shi +5
Low Earth Orbit (LEO) satellite networks serve as a cornerstone infrastructure for providing ubiquitous connectivity in areas where terrestrial infrastructure is unavailable. With…