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arXiv 2023年

作者： Seyedi, Zahra Rahmati, Farhad Ali, Mohammad Liu, Ximeng Department of Mathematics and Computer Science Amirkabir University of Technology Tehran Iran Key Laboratory of Information Security of Network Systems College of Computer and Data Science Fuzhou University Fuzhou350108 China

Edge storage presents a viable data storage alternative for application vendors (AV), offering benefits such as reduced bandwidth overhead and latency compared to cloud storage. However, data cached in edge computing systems is susceptible to intentional or accidental disturbances. This paper proposes a decentralized integrity auditing scheme to safeguard data integrity and counter the traditional reliance on centralized third-party auditors (TPA), which are unfit for distributed systems. Our novel approach employs edge servers (ES) as mutual auditors, eliminating the need for a centralized entity. This decentralization minimizes potential collusion with malicious auditors and biases in audit outcomes. Using a strategic game model, we demonstrate that ESs are more motivated to audit each other than TPAs. The auditing process is addressed as a Nash Equilibrium problem, assuring accurate integrity proof through incentives for ESs. Our scheme’s security and performance are rigorously assessed, showing it is secure within the random oracle model, offers improved speed, and is cost-effective compared to existing methods. © 2023, CC0.

关键词： Game theory

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Cross-Chain Asset Transaction Method Based on Decentralized Digital Identity

Cross-Chain Asset Transaction Method Based on Decentralized ...

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Blockchain Technology and information Security (ICBCTIS), International Conference on

作者： Ruiyao Zhou Shuhui Zhang Lianhai Wang Shujiang Xu Wei Shao Ting Gao Ruixue Kuang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China

ISBN: (数字)9798331528324

ISBN: (纸本)9798331528331

The swift advancement of blockchain technology has improved the transfer of value and assets between various blockchain systems. Nonetheless, the differing underlying architectures of these platforms have impeded effective value transfers, leading to the development of cross-chain technology. Cross-chain transactions face complexities due to varied user identity definitions, methods, and authentication mechanisms across blockchain networks. To address these challenges, this study optimizes identity identification and authentication methods with a privacy-preserving solution for cross-chain asset transactions. This enhances the identity management mechanism, safeguarding user privacy. Initially using ring signature algorithms to anonymize user identities, the study now employs Decentralized Identity (DID) and Verifiable Credentials (VC) for unified identity identification and authentication across chains. This approach grants users control over their identities, removes blockchain barriers, and adapts to multiple scenarios. Comparative analysis includes gas consumption, transaction delays, and performance evaluations of both solutions.

关键词： Performance evaluation Privacy Refining Authentication information security Blockchains Fraud Relays Protection Interoperability

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Gradient-YOLO: Exploring the integration of gradient architecture into the YOLO network

Gradient-YOLO: Exploring the integration of gradient archite...

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International Joint Conference on Neural networks (IJCNN)

作者： Gang Li Cheng Zhang Delong Han Letian Gao Mingle Zhou Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China SHANDONG SCICOM Information and Economy Research Institute Co. Ltd Jinan China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Object detection is an essential task in the field of computer vision. The one-stage object detection model directly completes object detection through a single forward propagation and has fast real-time object detection capabilities, making it widely used—especially the model of the You Only Look Once (YOLO) series. Improving the detection accuracy of the YOLO model has always been a research topic. The gradient architecture cascades feature information extraction and aggregates all features at the end, which can improve the feature fusion ability of the network. Combining the idea of gradient architecture, this paper proposes a YOLO based on gradient architecture: Gradient-YOLO. Specifically, this paper integrates gradient architecture into the backbone, neck, and bottleneck structures in the YOLO network, obtaining gradient backbone, gradient neck, and gradient bottleneck, respectively. By combining gradient architecture, various network parts in Gradient-YOLO can fully aggregate multi-layer features, reduce feature loss, and thus improve detection accuracy. This paper takes the mature YOLOv5 model and the latest YOLOv8 model as the baseline and combines gradient thinking to obtain Gradient-YOLOv5 and GradientYOLOv8. Moreover, conduct experimental testing on the MS COCO dataset. Regarding the mAP@.5 detection indicator, compared to YOLOv5s, Gradient-YOLOv5s increased by 5.07%. Compared to YOLOv8n, Gradient-YOLOv8n has increased by 4.63%. Therefore, the combination of gradient architecture can improve the detection accuracy of YOLO networks.

关键词： YOLO Accuracy Aggregates Computational modeling computer architecture Feature extraction Real-time systems

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Energy-and-Spectrum-Efficient Trade-off Oriented Multi-Domains Resource Management Approach in RIS-Aided IABN Using NOMA With Imperfect SIC

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green Communications and networking 2025年

作者： Ma, Zhongyu Li, Jing Han, Guangjie Wang, Yajing Liu, Zhirong Hao, Zhanjun Guo, Qun Northwest Normal University College of Computer Science and Engineering Lanzhou730070 China Hohai University Key Laboratory of Maritime Intelligent Network Information Technology Ministry of Education 213000 China Northwestern Polytechnical University School of Electronic Information Xi'an710129 China Lanzhou University of Technology College of Electrical and Information Engineering Lanzhou730050 China

Although reconfigurable intelligent surface (RIS)-assisted millimeter-wave integrated access and backhaul networks (mmWave IABN) offer a cost-effective solution for 6G systems, they face difficulties in meeting the spectral efficiency (SE) and energy efficiency (EE) requirements due to environmental uncertainties and interference. This paper focuses on the application of RIS-aided mmWave IABN and proposes an energy-and-spectrum-efficient trade-off oriented multi-domains resource management (ETOM-RM) scheme to address these challenges by optimizing the trade-off between SE and EE. The scheme uses non-orthogonal multiple access (NOMA) technology combined with imperfect successive interference cancellation (SIC), and it decomposes the optimization problem into two sub-problems: user transmission power and RIS phase-shift control, as well as bandwidth partition between access and backhaul. Simulation results validate the ETOM-RM scheme's effectiveness in enhancing SE and optimizing the SE-EE trade-off. Compared to the OMA-RMM and RPS-RMM schemes, it demonstrates superior performance. Additionally, the observed trade-off between SE and EE underscores the scheme's robustness across diverse scenarios, which provides concrete guidelines for advancing environmentally sustainable wireless communication technologies. © 2017 IEEE.

关键词： Resource allocation

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Securely and Efficiently Outsourcing Neural network Inference via Parallel MSB Extraction

Securely and Efficiently Outsourcing Neural Network Inferenc...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Xin Liu Ning Xi Ke Cheng Jiaxuan Fu Xinghui Zhu Yulong Shen Jianfeng Ma School of Computer Science and Technology Xidian University Xi’an China School of Cyber Engineering Xidian University Xi’an China Hebei Key Laboratory of Network and Information Security Hebei Normal University China

Outsourcing neural network (NN) inference services to the cloud gives rise to considerable privacy concerns about the model provider’s proprietary model and the user’s private data. Current cryptography-based secure NN inference schemes are not suited for high-latency networks due to their numerous communication overhead for computing the non-linear components of neural networks. In this paper, we present ParaNN, a secure cloud-based outsourced computation framework that supports lightweight secure neural network inference. At the core of ParaNN, we design a secure and parallel method for extracting the most significant bit (MSB) based on a parallel prefix adder. This forms the cornerstone for a series of secure and communication-efficient computation protocols specifically tailored to non-linear layers like ReLU and Maxpool. Our experiments show that ParaNN achieves a 6.7×-27.4× improvement in online inference time over wide area networks (WAN) compared to the state-of-the-art works.

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Block-based fine-grained and publicly verifiable data deletion for cloud storage: Block-based fine-grained and publicly verifiable..: C. Yang et al.

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Soft Computing 2024年第21期28卷 12491-12506页

作者： Yang, Changsong Liu, Yueling Ding, Yong Wu, Yongqiang Guangxi Key Laboratory of Cryptography and Information Security Guilin University of Electronic Technology Guilin541004 China Lion Rock Labs of Cyberspace Security Institute of Cyberspace Technology HKCT Institute for Higher Education 999077 Hong Kong Guangxi Engineering Research Center of Industrial Internet Security and Blockchain Guilin University of Electronic Technology Guilin541004 China School of Computer Guangdong University of Science & Technology Dongguan523083 China South China Technical Support Department Topsec Network Technology Inc. Guangzhou510000 China

One of the most important services provided by cloud service providers (CSPs), cloud storage is economically attractive and can provide on-demand data storage service to resource-constrained tenants in the manner of pay-per-use. Therefore, by embracing cloud storage service, tenants can store their large-scale data on the remote cloud host to tremendously reduce local storage burden and computational overhead. However, due to the separation of cloud data ownership and management, cloud storage inevitably suffers from a few new security challenges, such as fine-grained cloud data deletion. To achieve data deletion in cloud storage, we design a vector-commitment-based publicly verifiable data deletion scheme, which can achieve data integrity auditing and block-based fine-grained data deletion. Specifically, our proposed scheme disguises the data deletion operation as data update operation. Therefore, the tenants can achieve data deletion by updating the cloud data blocks with some unrelated data blocks. Meanwhile, the tenants can prevent cloud data from being polluted by periodically performing data integrity auditing operation, thus guaranteeing that the cloud host sincerely maintains the data. Moreover, we formally analyze the security, which shows that our proposed scheme can satisfy all of the anticipative security requirements without interaction with a centralized third party. Finally, we develop a prototype system and evaluate the performance of our proposed scheme, which can intuitively demonstrate the high-efficiency and practicality of our proposal. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Data integrity

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Continual Reinforcement Learning for Digital Twin Synchronization Optimization

arXiv

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arXiv 2025年

作者： Tong, Haonan Chen, Mingzhe Zhao, Jun Hu, Ye Yang, Zhaohui Liu, Yuchen Yin, Changchuan The Beijing Key Laboratory of Network System Architecture and Convergence The Beijing Advanced Information Network Laboratory Beijing University of Posts and Telecommunications Beijing China Singapore The Department of Electrical and Computer Engineering Frost Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States The Department of Industrial and Systems Engineering University of Miami Coral GablesFL33146 United States The College of Information Science and Electronic Engineering Zhejiang University Zhejiang Hangzhou310027 China The Department of Computer Science North Carolina State University RaleighNC27695 United States The Beijing Key Laboratory of Network System Architecture and Convergence China The Beijing Advanced Information Network Laboratory Beijing University of Posts and Telecommunications Beijing100876 China

This article investigates the adaptive resource allocation scheme for digital twin (DT) synchronization optimization over dynamic wireless networks. In our considered model, a base station (BS) continuously collects factory physical object state data from wireless devices to build a real-time virtual DT system for factory event analysis. Due to continuous data transmission, maintaining DT synchronization must use extensive wireless resources. To address this issue, a subset of devices is selected to transmit their sensing data, and resource block (RB) allocation is optimized. This problem is formulated as a constrained Markov process (CMDP) problem that minimizes the long-term mismatch between the physical and virtual systems. To solve this CMDP, we first transform the problem into a dual problem that refines RB constraint impacts on device scheduling strategies. We then propose a continual reinforcement learning (CRL) algorithm to solve the dual problem. The CRL algorithm learns a stable policy across historical experiences for quick adaptation to dynamics in physical states and network capacity. Simulation results show that the CRL can adapt quickly to network capacity changes and reduce normalized root mean square error (NRMSE) between physical and virtual states by up to 55.2%, using the same RB number as traditional methods. © 2025, CC BY-NC-SA.

关键词： Markov processes

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Robust Video Watermarking network Based on Channel Spatial Attention

Robust Video Watermarking Network Based on Channel Spatial A...

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International Joint Conference on Neural networks (IJCNN)

作者： Jian Li Tao Zuo Bin Ma Chunpeng Wang Peng Zhang Huanhuan Zhao Zhengzhong Zhao Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Robust video watermarking refers to the ability to extract the originally embedded watermark information from a video even after malicious modifications and attacks. Currently, traditional watermarking methods have the drawback of lacking robustness against multiple watermark attacks simultaneously. Neural network-based approaches have not fully considered the multi-scale features of videos and tend to lose information during the fusion of scale features. Therefore, we propose a video watermarking scheme based on Channel Spatial Attention. Our model can extract feature information at different scales, allowing the watermark to adapt to features of different scales in the video. Through a series of comparative experiments, our method has shown significant improvements over traditional video watermarking methods and deep learning-based video watermarking models.

关键词： Adaptation models Convolution Video sequences Neural networks Watermarking Interference Feature extraction

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Large-Scale Satellite Optical network Simulation Architecture and Performance Evaluation

Large-Scale Satellite Optical Network Simulation Architectur...

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Asia Communications and Photonics Conference and Exhibition (ACP)

作者： Xinjun Wang Zhenrong Zhang Kangqi Zhu Nan Hua Guangxi Key Laboratory of Multimedia Communications and Network Technology School of Computer Electronics and Information Guangxi University Nanning China Department of Electronic Engineering Beijing National Research Center for Information Science and Technology (BNRist) Tsinghua University Beijing China

We evaluate the accuracy and performance of different simulation modes in MegaStarSim, analyze end-to-end routing strategies in large-scale satellite optical networks, and assess link quality across various inter-sate... 详细信息

ISBN: (数字)9798350379266

ISBN: (纸本)9798350379273

关键词： Performance evaluation Satellites Accuracy Simulation computer architecture Optical fiber networks Routing Software Photonics Graphical user interfaces

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High-quality Arabidopsis thaliana Genome Assembly with Nanopore and HiFi Long Reads

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Genomics, Proteomics & Bioinformatics 2022年第1期20卷 4-13页

作者： Bo Wang Xiaofei Yang Yanyan Jia Yu Xu Peng Jia Ningxin Dang Songbo Wang Tun Xu Xixi Zhao Shenghan Gao Quanbin Dong Kai Ye MOE Key Laboratory for Intelligent Networks&Network Security Faculty of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an 710049China School of Computer Science and Technology Faculty of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an 710049China School of Life Science and Technology Xi’an Jiaotong UniversityXi’an 710049China School of Automation Science and Engineering Faculty of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an 710049China Genome Institute the First Affiliated Hospital of Xi’an Jiaotong UniversityXi’an 710061China

Arabidopsis thaliana is an important and long-established model species for plant molecular biology,genetics,epigenetics,and ***,the latest version of reference genome still contains a significant number of missing ***,we reported a high-quality and almost complete Col-0 genome assembly with two gaps(named Col-XJTU)by combining the Oxford Nanopore Technologies ultra-long reads,Pacific Biosciences high-fidelity long reads,and Hi-C *** total genome assembly size is 133,725,193 bp,introducing 14.6 Mb of novel sequences compared to the TAIR10.1 reference *** five chromosomes of the Col-XJTU assembly are highly accurate with consensus quality(QV)scores>60(ranging from 62 to 68),which are higher than those of the TAIR10.1 reference(ranging from 45 to 52).We completely resolved chromosome(Chr)3 and Chr5 in a telomere-to-telomere ***4 was completely resolved except the nucleolar organizing regions,which comprise long repetitive DNA *** Chrl centromere(CEN1),reportedly around 9 Mb in length,is particularly challenging to assemble due to the presence of tens of thousands of CEN180 satellite *** the cutting-edge sequencing data and novel computational approaches,we assembled a 3.8-Mb-long CEN1 and a 3.5-Mb-long *** also investigated the structure and epigenetics of *** clusters of CEN180 monomers were detected,and the centromere-specific histone H3-like protein(CENH3)exhibited a strong preference for CEN180 Cluster ***,we observed hypomethylation patterns in CENH3-enriched *** believe that this high-quality genome assembly,Col-XJTU,would serve as a valuable reference to better understand the global pattern of centromeric polymorphisms,as well as the genetic and epigenetic features in plants.

关键词： Centromere architecture CENH3 Bacterial artificial chromosome Telomere-to-telomere Model plant

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