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arXiv 2024年

作者： Li, Haoyang Chen, Wei Zhang, Xiaojin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Laboratory Cluster and Grid Computing Laboratory School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China School of Software Engineering Huazhong University of Science and Technology Wuhan430074 China

Gradient leakage attacks pose a significant threat to the privacy guarantees of federated learning. While distortion-based protection mechanisms are commonly employed to mitigate this issue, they often lead to notable performance degradation. Existing methods struggle to preserve model performance while ensuring privacy. To address this challenge, we propose a novel data augmentation-based framework designed to achieve a favorable privacy-utility trade-off, with the potential to enhance model performance in certain cases. Our framework incorporates the AugMix algorithm at the client level, enabling data augmentation with controllable severity. By integrating the Jensen-Shannon divergence into the loss function, we embed the distortion introduced by AugMix into the model gradients, effectively safeguarding privacy against deep leakage attacks. Moreover, the JS divergence promotes model consistency across different augmentations of the same image, enhancing both robustness and performance. Extensive experiments on benchmark datasets demonstrate the effectiveness and stability of our method in protecting privacy. Furthermore, our approach maintains, and in some cases improves, model performance, showcasing its ability to achieve a robust privacy-utility trade-off. Copyright © 2024, The Authors. All rights reserved.

关键词： Differential privacy

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On efficient bandwidth allocation for traffic variability in datacenters

On efficient bandwidth allocation for traffic variability in...

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IEEE Annual Joint Conference: INFOCOM, IEEE Computer and Communications Societies

作者： Jian Guo Fangming Liu Xiaomeng Huang John C. S. Lui Mi Hu Qiao Gao Hai Jin Key Laboratory of Services Computing Technology and System Huazhong University of Science and Technology Ministry of Education Key Laboratory for Earth System Modeling Tsinghua University Department of Computer Science & Engineering The Chinese University of Hong Kong

Datacenter networks suffer unpredictable performance due to a lack of application level bandwidth guarantees. A lot of attentions have been drawn to solve this problem such as how to provide bandwidth guarantees for Virtualized Machines (VMs), proportional bandwidth share among tenants, and high network utilization under peak traffic. However, existing solutions fail to cope with highly dynamic traffic in datacenter networks. In this paper, we consider the effects of large numbers of short flows and massive bursty traffic in the datacenter, and design a novel distributed rate allocation algorithm based on the Logistic model under the control-theoretic framework. The theoretical analysis and experimental results using OpenFlow show that our algorithm not only guarantees the bandwidth for VMs, but also provides fast convergence to efficiency and fairness, and smooth response to bursty traffic.

关键词： Bandwidth Convergence Heuristic algorithms Algorithm design and analysis Resource management Channel allocation Logistics

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Non-invasive biopsy diagnosis of diabetic kidney disease via deep learning applied to retinal images: a population-based study

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The Lancet Digital Health 2025年第5期7卷 100868页

作者： Meng, Ziyao Guan, Zhouyu Yu, Shujie Wu, Yilan Zhao, Yaoning Shen, Jie Lim, Cynthia Ciwei Chen, Tingli Yang, Dawei Ran, An Ran He, Feng Hamzah, Haslina Singh, Sarkaaj Abd Raof, Anis Syazwani Lee-Boey, Jian Wen Samuel Lim, Soo-Kun Sun, Xufang Ge, Shuwang Xu, Gang Su, Hua Cheng, Yang Lu, Feng Liao, Xiaofei Jin, Hai Deng, Chenxin Ruan, Lei Zhang, Cuntai Wu, Chan Dai, Rongping Jin, Yixiao Wang, Wenxiao Li, Tingyao Liu, Ruhan Li, Jiajia Shu, Jia Lu, Yuwei Wang, Xiangning Wu, Qiang Qin, Yiming Tang, Jin Sheng, Xiaohua Jiao, Qiong Yang, Xiaokang Guo, Minyi McKay, Gareth J Hogg, Ruth E Liew, Gerald Chee, Evelyn Yi Lyn Hsu, Wynne Lee, Mong Li Szeto, Simon Luk, Andrea O Y Chan, Juliana C N Cheung, Carol Y Tan, Gavin Siew Wei Tham, Yih-Chung Cheng, Ching-Yu Sabanayagam, Charumathi Lim, Lee-Ling Jia, Weiping Li, Huating Sheng, Bin Wong, Tien Yin Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases Department of Computer Science and Engineering School of Electronic Information and Electrical Engineering Institute for Proactive Healthcare 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 Centre for Diabetes Shanghai Key Laboratory of Diabetes Mellitus Shanghai China MOE Key Laboratory of AI School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Beijing Tsinghua Changgung Hospital Eye Center School of Clinical Medicine Tsinghua Medicine Tsinghua University 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 Renal Medicine Singapore General Hospital SingHealth-Duke Academic Medical Centre Singapore Singapore Department of Ophthalmology Shanghai Health and Medical Center Wuxi China Department of Ophthalmology and Visual Sciences The Chinese University of Hong Kong Hong Kong Special Administrative Region China Singapore Eye Research Institute Singapore National Eye Centre Singapore Department of Medicine Faculty of Medicine Universiti Malaya Kuala Lumpur Malaysia Department of Ophthalmology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Hubei Wuhan China Department of Nephrology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China Department of Nephrology Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China Department of Ophthalmology Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China National Engineering Research Centre for Big Dat

Background: Improving the accessibility of screening diabetic kidney disease (DKD) and differentiating isolated diabetic nephropathy from non-diabetic kidney disease (NDKD) are two major challenges in the field of diabetes care. We aimed to develop and validate an artificial intelligence (AI) deep learning system to detect DKD and isolated diabetic nephropathy from retinal fundus images. Methods: In this population-based study, we developed a retinal image-based AI-deep learning system, DeepDKD, pretrained using 734 084 retinal fundus images. First, for DKD detection, we used 486 312 retinal images from 121 578 participants in the Shanghai Integrated Diabetes Prevention and Care system for development and internal validation, and ten multi-ethnic datasets from China, Singapore, Malaysia, Australia, and the UK (65 406 participants) for external validation. Second, to differentiate isolated diabetic nephropathy from NDKD, we used 1068 retinal images from 267 participants for development and internal validation, and three multi-ethnic datasets from China, Malaysia, and the UK (244 participants) for external validation. Finally, we conducted two proof-of-concept studies: a prospective real-world study with 3 months' follow-up to evaluate the effectiveness of DeepDKD in screening DKD;and a longitudinal analysis of the effectiveness of DeepDKD in differentiating isolated diabetic nephropathy from NDKD on renal function changes with 4·6 years' follow-up. Findings: For detecting DKD, DeepDKD achieved an area under the receiver operating characteristic curve (AUC) of 0·842 (95% CI 0·838–0·846) on the internal validation dataset and AUCs of 0·791–0·826 across external validation datasets. For differentiating isolated diabetic nephropathy from NDKD, DeepDKD achieved an AUC of 0·906 (0·825–0·966) on the internal validation dataset and AUCs of 0·733–0·844 across external validation datasets. In the prospective study, compared with the metadata model, DeepDKD could detect DKD wit

关键词： Deep learning

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A Cooperative Game Based Allocation for Sharing Data Center Networks

A Cooperative Game Based Allocation for Sharing Data Center ...

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IEEE INFOCOM Conference

作者： Jian Guo Fangming Liu Dan Zeng John C. S. Lui Hai Jin Key Laboratory of Services Computing Technology and System Ministry of Education School of Computer Science and Technology Huazhong University of Science and Technology China The Chinese University of Hong Kong

ISBN: (纸本)9781467359443

In current IaaS datacenters, tenants are suffering unfairness since the network bandwidth is shared in a best-effort manner. To achieve predictable network performance for rented virtual machines (VMs), cloud providers should guarantee minimum bandwidth for VMs or allocate the network bandwidth in a fairness fashion at VM-level. At the same time, the network should be efficiently utilized in order to maximize cloud providers' revenue. In this paper, we model the bandwidth sharing problem as a Nash bargaining game, and propose the allocation principles by defining a tunable base bandwidth for each VM. Specifically, we guarantee bandwidth for those VMs with lower network rates than their base bandwidth, while maintaining fairness among other VMs with higher network rates than their base bandwidth. Based on rigorous cooperative game-theoretic approaches, we design a distributed algorithm to achieve efficient and fair bandwidth allocation corresponding to the Nash bargaining solution (NBS). With simulations under typical scenarios, we show that our strategy can meet the two desirable requirements towards predictable performance for tenants as well as high utilization for providers. And by tuning the base bandwidth, our solution can enable cloud providers to flexibly balance the tradeoff between minimum guarantees and fair sharing of datacenter networks.

关键词： Bandwidth Servers Channel allocation Resource management Games Bismuth NIST

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JSRevealer: A Robust Malicious JavaScript Detector against Obfuscation

JSRevealer: A Robust Malicious JavaScript Detector against O...

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International Conference on Dependable systems and Networks (DSN)

作者： Kunlun Ren Weizhong Qiang Yueming Wu Yi Zhou Deqing Zou Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Jinyinhu Laboratory Wuhan China Nanyang Technological University Singapore 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 Technology Huazhong University of Science and Technology Wuhan China

Due to the convenience and popularity of Web applications, they have become a prime target for attackers. As the main programming language for Web applications, many methods have been proposed for detecting malicious JavaScript, among which static analysis-based methods play an important role because of their high effectiveness and efficiency. However, obfuscation techniques are commonly used in JavaScript, which makes the features extracted by static analysis contain many useless and disguised features, leading to many false positives and false negatives in detection results. In this paper, we propose a novel method to find out the essential features related to the semantics of JavaScript code. Specifically, we develop JS-Revealer, a robust, effective, scalable, and interpretable detector for malicious JavaScript. To test the capabilities of JSRevealer, we conduct comparative experiments with four other state-of-the-art malicious JavaScript detection tools. The experimental results show that JSRevealer has an average F1 of 84.8% on the data obfuscated by different obfuscators, which is 21.6%, 22.3%, 18.7%, and 22.9% higher than the tools CUJO, ZOZZLE, JAST, and JSTAP, respectively. Moreover, the detection results of JSRevealer can be interpreted, which can provide meaningful insights for further security research.

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LEEN: Locality/Fairness-Aware Key Partitioning for MapReduce in the Cloud

LEEN: Locality/Fairness-Aware Key Partitioning for MapReduce...

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IEEE International Conference on Cloud computing technology and Science (CloudCom)

作者： Shadi Ibrahim Hai Jin Lu Lu Song Wu Bingsheng He Li Qi Cluster and Grid Computing Laboratory Services Computing Technology and System Laboratory Huazhong University of Science and Technology Wuhan China School of Computer Engineering Nanyang Technological University Singapore Information and Technology Department China Development Bank Beijing China

This paper investigates the problem of Partitioning Skew in MapReduce-based system. Our studies with Hadoop, a widely used MapReduce implementation, demonstrate that the presence of partitioning skew causes a huge amount of data transfer during the shuffle phase and leads to significant unfairness on the reduce input among different data nodes. As a result, the applications experience performance degradation due to the long data transfer during the shuffle phase along with the computation skew, particularly in reduce phase. We develop a novel algorithm named LEEN for locality-aware and fairness-aware key partitioning in MapReduce. LEEN embraces an asynchronous map and reduce scheme. All buffered intermediate keys are partitioned according to their frequencies and the fairness of the expected data distribution after the shuffle phase. We have integrated LEEN into Hadoop-0.18.0. Our experiments demonstrate that LEEN can efficiently achieve higher locality and reduce the amount of shuffled data. More importantly, LEEN guarantees fair distribution of the reduce inputs. As a result, LEEN achieves a performance improvement of up to 40% on different workloads.

关键词： Partitioning algorithms Distributed databases Cloud computing Performance evaluation Time factors Degradation Kernel

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VMDriver: A Driver-Based Monitoring Mechanism for Virtualization

VMDriver: A Driver-Based Monitoring Mechanism for Virtualiza...

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Reliable Distributed systems

作者： Guofu Xiang Hai Jin Deqing Zou Xinwen Zhang Sha Wen Feng Zhao Cluster and Grid Computing Laboratory Services Computing Technology and System Lab Huazhong University of Science and Technology Wuhan China Samsung Information Systems America Inc. San Jose CA USA

Monitoring virtual machine (VM) is an essential function for virtualized platforms. Existing solutions are either coarse-grained - monitoring in granularity of VM level, or not general - only support specific monitoring functions for particular guest operating system (OS). Thus they do not satisfy the monitoring requirement in large-scale server cluster such as data center and public cloud platform, where each physical platform runs hundreds of VMs with different guest OSes. In this paper, we propose VMDriver, a general and fine-grained approach for virtualization monitoring. The novel design of VMDriver is the separation of event interception point in VMM level and rich guest OS semantic reconstructions in management domain. With this design, variant monitoring drivers in management domain can mask the differences of guest OSes. We implement VMDriver on Xen and our experimental study shows that it introduces very small performance overhead. We demonstrate its generality by inspecting four aspects information about the target virtual machines with different guest OSes. The unified interface of VMDriver brings convenience to develop complex monitoring tools for distributed virtualization environment.

关键词： Monitoring Driver circuits Semantics Linux Kernel Malware

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Data Hiding in Virtual Machine Disk Images

Data Hiding in Virtual Machine Disk Images

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International Conference on Computer and Information technology (CIT)

作者： Yu Su Xiaofei Liao Hai Jin Tim Bell Cluster and Grid Computing Laboratory Services Computing Technology and System Laboratory School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Department of Computer Science and Software Engineering College of Engineering University of Canterbury Christchurch New Zealand

Data hiding in computer system is an interesting and important research issue, which brings benefits for secret communication and watermarking. The development of virtual machine brings new potential for data hiding. In this paper we explore the potential for data hiding in virtual machine disk images, and especially hiding schemes that can be used with copy-on-write images. Besides being considered as a way for valid uses such as secret communication and watermarking, these schemes can be a warning against malicious intentions as well. Furthermore, it lays the foundation for a more thorough analysis of the whole virtual machine system for data hiding.

关键词： File systems Detectors Writing Virtual machining Linux Fats Virtual environment

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Probabilistic Critical Path Identification for Cost-Effective Monitoring of Service-Based systems

Probabilistic Critical Path Identification for Cost-Effectiv...

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IEEE International Conference on services computing (SCC)

作者： Qiang He Jun Han Yun Yang Jean-Guy Schneider Hai Jin Steve Versteeg Faculty of Information and Communication Technologies Swinburne University of Technology Melbourne Australia Services Computing Technology and System Laboratory Cluster and Grid Computing Laboratory School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China CA Laboratories Melbourne Australia

When operating in volatile environments, service-based systems (SBSs) that are built through dynamic composition of component services must be monitored in order to guarantee the response times of the SBSs. In particular, the critical path of a composite SBS, i.e., the execution path in the service composition with the maximum execution time, should be prioritised in cost-effective monitoring as it determines the response time of the SBS. In volatile operating environments, the critical path of a SBS is probabilistic. As such, it is important to estimate the criticalities of the execution paths and the component services, i.e., the probabilities that they are critical, to decide which parts of the system to monitor. In this paper, we propose a novel approach to the identification of Probabilistic Critical Path for Service-based systems (PCP-SBS). PCP-SBS takes into account the probabilistic nature of the critical path and calculates path criticalities in the context of service composition. We evaluate PCP-SBS experimentally using SBSs that are synthetically composed based on a real-world Web service dataset.

关键词： Monitoring Scattering Time factors Timing Probability Probabilistic logic Runtime

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Dynamic SDN Controller Assignment in Data Center Networks: Stable Matching with Transfers

Dynamic SDN Controller Assignment in Data Center Networks: S...

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Annual IEEE International Conference on Computer Communications

作者： Tao Wang Fangming Liu Jian Guo Hong Xu Key Laboratory of Services Computing Technology and System Ministry of Education School of Computer Science and Technology Huazhong University of Science and Technology NetX Lab @ City University of Hong Kong

ISBN: (纸本)9781467399548

Software defined networking is becoming increasingly prevalent in data center networks for its programmability that enables centralized network configuration and management. However, since switches are statically assigned to controllers, traffic dynamics cause load imbalance among the controllers. As a result, some controllers are not fully utilized, while switches connected to overloaded controllers may experience long response times. In this paper, we consider dynamic controller assignment so as to minimize the average response time of the control plane. We formulate this problem as a stable matching problem with transfers, and propose a hierarchically two-phase algorithm that integrates key concepts from both matching theory and coalitional games to solve it efficiently. Theoretical analysis proves that our algorithm converges to a near-optimal Nash stable solution within tens of iterations. Extensive simulations show that our approach reduces response time by about 86%, and achieves better load balancing among controllers compared to static assignment.

关键词： Controllers Reaction Time control surfaces Switches data center unbalanced load programmability

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