4 citations · 4 across the 12 of their papers we have counts for
12 papers
Sparse Threats, Focused Defense: Criticality-Aware Robust Reinforcement Learning for Safe Autonomous Driving
Qi Wei, Junchao Fan, Zhao Yang +3
Reinforcement learning (RL) has shown considerable potential in autonomous driving (AD), yet its vulnerability to perturbations remains a critical barrier to real-world deployment.…
Robust Driving Control for Autonomous Vehicles: An Intelligent General-sum Constrained Adversarial Reinforcement Learning Approach
Junchao Fan, Qi Wei, Ruichen Zhang +4
Deep reinforcement learning (DRL) has demonstrated remarkable success in developing autonomous driving policies. However, its vulnerability to adversarial attacks remains a critica…
Towards Reliable Service Provisioning for Dynamic UAV Clusters in Low-Altitude Economy Networks
Yanwei Gong, Ruichen Zhang, Xiaoqing Wang +5
Unmanned Aerial Vehicle (UAV) cluster services are crucial for promoting the low-altitude economy by enabling scalable, flexible, and adaptive aerial networks. To meet diverse serv…
DRAGD: A Federated Unlearning Data Reconstruction Attack Based on Gradient Differences
Bocheng Ju, Junchao Fan, Jiaqi Liu +1
Federated learning enables collaborative machine learning while preserving data privacy. However, the rise of federated unlearning, designed to allow clients to erase their data fr…
Efficient and High-Accuracy Private CNN Inference with Helper-Assisted Malicious Security
Kaiwen Wang, Xiaolin Chang, Junchao Fan +1
Machine Learning as a Service (MLaaS) exposes sensitive client data to service providers. Private inference mitigates this risk while preserving model functionality. Despite extens…
Sharpening the Spear: Adaptive Expert-Guided Adversarial Attack Against DRL-based Autonomous Driving Policies
Junchao Fan, Xuyang Lei, Xiaolin Chang
Deep reinforcement learning (DRL) has emerged as a promising paradigm for autonomous driving. However, despite their advanced capabilities, DRL-based policies remain highly vulnera…
Safe and Economical UAV Trajectory Planning in Low-Altitude Airspace: A Hybrid DRL-LLM Approach with Compliance Awareness
Yanwei Gong, Junchao Fan, Ruichen Zhang +3
The rapid growth of the low-altitude economy has driven the widespread adoption of unmanned aerial vehicles (UAVs). This growing deployment presents new challenges for UAV trajecto…
TPM2.0-Supported Runtime Customizable TEE on FPGA-SoC with User-Controllable vTPM
Jingkai Mao, Xiaolin Chang
Constructing a Trusted Execution Environment (TEE) on Field Programmable Gate Array System on Chip (FPGA-SoC) in Cloud can effectively protect users' private intel-lectual Property…
Towards Lightweight and Privacy-preserving Data Provision in Digital Forensics for Driverless Taxi
Yanwei Gong, Xiaolin Chang, Jelena Mišić +3
Data provision, referring to the data upload and data access, is one key phase in vehicular digital forensics. The unique features of Driverless Taxi (DT) bring new issues to this…
MBCT: A Monero-Based Covert Transmission Approach with On-chain Dynamic Session Key Negotiation
Zhenshuai Yue, Haoran Zhu, Xiaolin Chang +3
Traditional covert transmission (CT) approaches have been hindering CT application while blockchain technology offers new avenue. Current blockchain-based CT approaches require off…
Towards Trust Proof for Secure Confidential Virtual Machines
Jingkai Mao, Haoran Zhu, Junchao Fan +2
The Virtual Machine (VM)-based Trusted-Execution-Environment (TEE) technology, like AMD Secure-Encrypted-Virtualization (SEV), enables the establishment of Confidential VMs (CVMs)…
Efficient and Privacy-Preserving Federated Learning based on Full Homomorphic Encryption
Yuqi Guo, Lin Li, Zhongxiang Zheng +4
Since the first theoretically feasible full homomorphic encryption (FHE) scheme was proposed in 2009, great progress has been achieved. These improvements have made FHE schemes com…