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Rongxing Lu
H指数:84
被引数:2.9万
个人简介
A Full Professor in the School of Computing at Queen's University. He previously served as Acting Director of the Canadian Institute for Cybersecurity (CIC) and held the Mastercard IoT Research Chair at the University of New Brunswick (UNB), where he was also a Professor in the Faculty of Computer Science. Prior to joining UNB, he was a tenure-track Assistant Professor in the School of Electrical and Electronic Engineering at Nanyang Technological University (NTU), Singapore (2013–2016), and completed a postdoctoral fellowship at the University of Waterloo (2012–2013). The scholar received a Ph.D. in Electrical and Computer Engineering from the University of Waterloo. Research focuses on IoT and big data security, privacy-enhancing technologies, and applied cryptography. The scholar is an IEEE Fellow and a highly cited scholar with an H-index of 98.
教育背景
2012.12
University of Waterloo PhD
2006.12
Shanghai Jiao Tong University PhD
工作经历
2013.01 – 2016.12
Nanyang Technological University (NTU), Singapore
Assistant Professor
2012.01 – 2013.12
University of Waterloo
Postdoctoral Fellow
荣誉奖项
- Governor General's Academic Gold Medal（2012）｜University of Waterloo
- IEEE Fellow｜IEEE
- Best Paper and Best Student Paper awards｜leading journals and international conferences
- IEEE ComSoc Distinguished Lecturer（2024–2025）｜IEEE ComSoc
- IEEE ComSoc Asia-Pacific Outstanding Young Researcher Award（2013）｜IEEE ComSoc
- Technical Recognition Award（2026）｜IEEE Communications and Information Security Technical Committee (CISTC)
- Excellence in Teaching Award（2016–2017）｜UNB
- Excellence in Teaching Award（2023–2024）｜UNB
SecGAT：云环境中高效且隐私保护的图注意力网络推理
障碍感知三维空间的隐私保护反向空间天际线查询方案
连接黑盒和无盒：在深度识别系统中嵌入重建攻击
车载群感中面向AoI的隐私保护任务分配
边缘辅助的隐私集合交集方案用于隐私保护的车载众包感知
SecMLOps：一种全面整合安全于机器学习运营生命周期的框架
SecureT2I：防止基于提示的AI生成图像的未授权篡改
全球发现：一种面向查询的多模态摘要的全球图-RAG方法，适用于多PDF论文
双服务器离线/在线私有信息检索（Two-Server Offline/Online Private Information Retrieval）与小型客户端存储
一种适用于IEC 61850变电站通信的匿名化框架：现场级和拓扑感知隐私
I2I后门：针对图像到图像任务的后门攻击
评估生成式推理模型在物联网生态系统中的凭证微调与轻量级客户端防御
新型安全稀疏内积及其在机器学习中的应用
可刺穿签名及其在隐私感知数据报告中的应用（面向VDTN）
面向云中垂直分布式数据的高效安全超立方树构建
评估针对中毒攻击的分离联邦学习的安全性和鲁棒性
