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检索条件"机构=Cluster and Grid Computing Laboratory"
260 条 记 录,以下是251-260 订阅
排序:
MISA: UNVEILING THE VULNERABILITIES IN SPLIT FEDERATED LEARNING
arXiv
收藏 引用
arXiv 2023年
作者: Wan, Wei Ning, Yuxuan Hu, Shengshan Xue, Lulu Li, Minghui Zhang, Leo Yu Jin, Hai School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Computer Science and Technology Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China School of Information and Communication Technology Griffith University Australia National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Hubei Key Laboratory of Distributed System Security China Hubei Engineering Research Center on Big Data Security China Cluster and Grid Computing Lab China
Federated learning (FL) and split learning (SL) are prevailing distributed paradigms in recent years. They both enable shared global model training while keeping data localized on users' devices. The former excels...
来源: 评论
Securely Fine-tuning Pre-trained Encoders Against Adversarial Examples
arXiv
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arXiv 2024年
作者: Zhou, Ziqi Li, Minghui Liu, Wei Hu, Shengshan Zhang, Yechao Wan, Wei Xue, Lulu Zhang, Leo Yu Yao, Dezhong Jin, Hai National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab. China Cluster and Grid Computing Lab. China Hubei Engineering Research Center on Big Data Security China Hubei Key Laboratory of Distributed System Security China School of Computer Science and Technology Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Information and Communication Technology Griffith University Australia
With the evolution of self-supervised learning, the pre-training paradigm has emerged as a predominant solution within the deep learning landscape. Model providers furnish pre-trained encoders designed to function as ... 详细信息
来源: 评论
Detecting Backdoors During the Inference Stage Based on Corruption Robustness Consistency
arXiv
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arXiv 2023年
作者: Liu, Xiaogeng Li, Minghui Wang, Haoyu Hu, Shengshan Ye, Dengpan Jin, Hai Wu, Libing Xiao, Chaowei School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Computer Science and Technology Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Hubei Key Laboratory of Distributed System Security China Hubei Engineering Research Center on Big Data Security China Cluster and Grid Computing Lab China School of Cyber Science and Engineering Wuhan University China Arizona State University United States
Deep neural networks are proven to be vulnerable to backdoor attacks. Detecting the trigger samples during the inference stage, i.e., the test-time trigger sample detection, can prevent the backdoor from being trigger... 详细信息
来源: 评论
Downstream-agnostic Adversarial Examples
arXiv
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arXiv 2023年
作者: Zhou, Ziqi Hu, Shengshan Zhao, Ruizhi Wang, Qian Zhang, Leo Yu Hou, Junhui Jin, Hai School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Computer Science and Technology Huazhong University of Science and Technology China School of Cyber Science and Engineering Wuhan University China School of Information and Communication Technology Griffith University Australia Department of Computer Science City University of Hong Kong Hong Kong National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security China Hubei Engineering Research Center on Big Data Security China Cluster and Grid Computing Lab
Self-supervised learning usually uses a large amount of unlabeled data to pre-train an encoder which can be used as a general-purpose feature extractor, such that downstream users only need to perform fine-tuning oper... 详细信息
来源: 评论
PointCA: Evaluating the Robustness of 3D Point Cloud Completion Models Against Adversarial Examples
arXiv
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arXiv 2022年
作者: Hu, Shengshan Zhang, Junwei Liu, Wei Hou, Junhui Li, Minghui Zhang, Leo Yu Jin, Hai Sun, Lichao School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Computer Science and Technology Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China Cluster and Grid Computing Lab China National Engineering Research Center for Big Data Technology and System Hubei Engineering Research Center on Big Data Security China Hubei Key Laboratory of Distributed System Security China Services Computing Technology and System Lab China City University of Hong Kong Hong Kong Deakin University Australia Lehigh University United States
Point cloud completion, as the upstream procedure of 3D recognition and segmentation, has become an essential part of many tasks such as navigation and scene understanding. While various point cloud completion models ... 详细信息
来源: 评论
The Power of Bamboo: On the Post-Compromise Security for Searchable Symmetric Encryption
arXiv
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arXiv 2024年
作者: Chen, Tianyang Xu, Peng Picek, Stjepan Luo, Bo Susilo, Willy Jin, Hai Liang, Kaitai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab China Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering China Cluster and Grid Computing Lab School of Computer Science and Technology China Huazhong University of Science and Technology Wuhan430074 China Digital Security Group Radboud University Nijmegen Netherlands Department of EECS Institute of Information Sciences The University of Kansas LawrenceKS United States Institute of Cybersecurity and Cryptology School of Computing and Information Technology University of Wollongong WollongongNSW2522 Australia Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft2628 CD Netherlands
Dynamic searchable symmetric encryption (DSSE) enables users to delegate the keyword search over dynamically updated encrypted databases to an honest-but-curious server without losing keyword privacy. This paper studi... 详细信息
来源: 评论
基于污点和概率的逃逸恶意软件多路径探索
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Security and Safety 2023年 第3期2卷 83-106页
作者: 徐钫洲 张网 羌卫中 金海 National Engineering Research Center for Big Data Technology and System Wuhan 430074China Services Computing Technology and System Lab Cluster and Grid Computing LabWuhan 430074China Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data SecurityWuhan 430074China School of Cyber Science and Engineering Huazhong University of Science and TechnologyWuhan 430074China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China Jinyinhu Laboratory Wuhan 430040China
Static analysis is often impeded by malware obfuscation techniques,such as encryption and packing,whereas dynamic analysis tends to be more resistant to obfuscation by leveraging concrete execution ***,malware can emp... 详细信息
来源: 评论
A deep learning system for predicting time to progression of diabetic retinopathy
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NATURE MEDICINE 2024年 第2期30卷 358-359页
作者: [Anonymous] Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders Department of Computer Science and Engineering School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Department of Endocrinology and Metabolism Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai Diabetes Institute Shanghai Clinical Center for Diabetes Shanghai China MOE Key Laboratory of AI School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Department of Ophthalmology Huadong Sanatorium Wuxi China Department of Ophthalmology Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Ophthalmology and Visual Sciences The Chinese University of Hong Kong Hong Kong China Singapore Eye Research Institute Singapore National Eye Centre Singapore Singapore Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong China Department of Chemical and Biological Engineering The Hong Kong University of Science and Technology Hong Kong China State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science Guangzhou China Department of Ophthalmology Peking Union Medical College Hospital Peking Union Medical College Chinese Academy of Medical Sciences Beijing China Medical Records and Statistics Office Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Geriatrics Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Tech
We developed and validated a deep learning system (termed DeepDR Plus) in a diverse, multiethnic, multi-country dataset to predict personalized risk and time to progression of diabetic retinopathy. We show that DeepDR... 详细信息
来源: 评论
高可扩展的RDF数据存储系统
高可扩展的RDF数据存储系统
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第29届中国数据库学术会议
作者: Yuan Pingpeng 袁平鹏 Liu Pu 刘谱 Zhang Wenya 张文娅 Wu Buwen 吴步文 Key Laboratory of Services Computing Technology and System(Huazhong University of Science and Technology) Ministry of Education Wuhan 430074 服务计算技术与系统教育部重点实验室(华中科技大学) 武汉430074 Key Laboratory of Cluster and Grid Computing of Hubei Province(Huazhong University of Science and Technology)Wuhan 430074 集群与网格计算湖北省重点实验室(华中科技大学) 武汉430074 School of Computer Science and Technology Huazhong University of Science and Technology Wuhan 430074 华中科技大学计算机科学与技术学院 武汉430074
由于资源描述框架(resource description framework,RDF)具有表达灵活、简洁等优点,已被接受为表达元数据及万维网上数据互联的规范.近年来,其数据量在以飞快的速度增长.相应地,要求存储RDF数据的系统应具有高扩展性.介绍了一个高可扩展... 详细信息
来源: 评论
大规模RDF数据库系统TripleBit
大规模RDF数据库系统TripleBit
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第29届中国数据库学术会议
作者: Liu Pu 刘谱 Wang Jing 王晶 Yuan Pingpen 袁平鹏 Wu Buwen 吴步文 Key Laboratoty of Services Computing Technology and System (Huazhong University of Science and Technology) Ministry of Education Wuhan 430074 服务计算技术与系统教育部重点实验室(华中科技大学) 武汉430074 Key Laboratory of Cluster and Grid Computing of Hubei Province(Huazhong University of Science and Technology)Wuhan 430074 集群与网格计算湖北省重点实验室(华中科技大学) 武汉430074 School of Computer Science and Technology Huazhong University of Science and Technology Wuhan 430074 华中科技大学计算机科学与技术学院 武汉430074
由于RDF(Resource Description Framework)具有表达灵活、简洁等优点,已被接受为表达元数据及万维网上数据互联的规范。近年来,其数据量以飞快的速度增长,相应地,要求存储RDF数据的系统应具有高扩展性。大规模RDF数据库系统TripleBit旨... 详细信息
来源: 评论