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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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Multimodal Multi-objective Particle Swarm Optimization Algorithm Based on Multi-directional Guidance 4

Multimodal Multi-objective Particle Swarm Optimization Algor...

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4th International Conference on High Performance Big Data and Intelligent Systems, HDIS 2022

作者： Li, Wei Gao, Yetong Wang, Lei School of Computer Science and Engineering Xi'an University of Technology Xi'an China Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an China

ISBN: (数字)9781665491440

ISBN: (纸本)9781665491440

In multimodal multi-objective optimization, the key issue is to find as many Pareto optimal solutions as possible and select promising solutions in the environmental selection. This paper proposes a multimodal multi-objective particle swarm optimization algorithm based on multi-directional guidance (MM-PSO-MG) to solve these problems. In the proposed algorithm, multi-directional guidance strategy is introduced to avoid premature convergence and find more Pareto optimal solutions. Moreover, the rank-based special crowding distance strategy is used to select promising solutions. 11 multimodal multi-objective test problems are used to verify the performance of the proposed algorithm. The results show that the proposed algorithm is competitive. © 2022 IEEE.

关键词： Multiobjective optimization

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LGDB-Net: Dual-Branch Path for Building Extraction from Remote Sensing Image 30

LGDB-Net: Dual-Branch Path for Building Extraction from Remo...

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30th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2024

作者： Zhang, Ronghuan Zhao, Jing Li, Ming Zou, Qingzhi Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China Shandong University of Traditional Chinese Medicine School of Intelligence and Information Engineering Jinan China

ISBN: (纸本)9798331515966

Extracting buildings from remote sensing images using deep learning techniques is a widely applied and crucial task. Convolutional Neural networks (CNNs) adopt hierarchical feature representation, showcasing powerful capabilities in extracting local information but facing challenges in capturing global features. Transformers can address this limitation, but they perform poorly in extracting local features and significantly increase memory requirements and computational complexity. To overcome these challenges, we propose a method for building extraction from remote sensing images called LGDB-Net (Local-Global Dual-Branch network), employing a dual-branch approach. Firstly, inspired by Swin Transformer, we designed GB-Former(Global Branch-Former) as the backbone network to model global information. We use a linear multi-head self-attention mechanism to reduce time and memory complexity while maintaining a large global receptive field. Additionally, we replace the traditional multi-layer perceptron with a convolution-enhanced multi-layer perceptron to improve channel feature representation, reduce model parameters, and enhance segmentation performance. Secondly, we use multiple Depth-wise Conv3×3 + LN (Layer Normalization) + GeLU (Gaussian Error Linear Unit) modules as the auxiliary branch for local detailed feature extraction. Finally, we adopt a multi-scale feature fusion strategy to integrate feature information from both branches. We conduct a series of experiments on three datasets: WHU, Massachusetts, and Inria. The experimental results demonstrate that the proposed method not only effectively improves segmentation accuracy with lower building omission and commission rates but also significantly reduces model parameters and computational complexity. © 2024 IEEE.

关键词： Convolutional neural networks

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FedIERF: Federated Incremental Extremely Random Forest for Wearable Health Monitoring

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Journal of computer Science & Technology 2023年第5期38卷 970-984页

作者：胡春雨忽丽莎袁林陆佃杰吕蕾陈益强 Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science Center(National Supercomputer Center in Jinan)Qilu University of Technology(Shandong Academy of Sciences)Jinan 250353China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer ScienceJinan 250000China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan 25000China Institute of Information Technology Hebei University of Economics and BusinessShijiazhuang 050061China School of Information Science and Engineering Shandong Normal UniversityJinan 250358China Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China

Wearable health monitoring is a crucial technical tool that offers early warning for chronic diseases due to its superior portability and low power ***,most wearable health data is distributed across dfferent organizations,such as hospitals,research institutes,and companies,and can only be accessed by the owners of the data in compliance with data privacy *** first challenge addressed in this paper is communicating in a privacy-preserving manner among different *** second technical challenge is handling the dynamic expansion of the federation without model *** address the first challenge,we propose a horizontal federated learning method called Federated Extremely Random Forest(FedERF).Its contribution-based splitting score computing mechanism significantly mitigates the impact of privacy protection constraints on model *** on FedERF,we present a federated incremental learning method called Federated Incremental Extremely Random Forest(FedIERF)to address the second technical *** introduces a hardness-driven weighting mechanism and an importance-based updating scheme to update the existing federated model *** experiments show that FedERF achieves comparable performance with non-federated methods,and FedIERF effectively addresses the dynamic expansion of the *** opens up opportunities for cooperation between different organizations in wearable health monitoring.

关键词： federated learning incremental learning random forest wearable health monitoring

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A Fault Diagnostic Method for Rolling Bearings Based on CAR-SNN Model

A Fault Diagnostic Method for Rolling Bearings Based on CAR-...

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2023 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes, SAFEPROCESS 2023

作者： Li, Qiuting Wang, Xiuqing Du, Wenxia Hebei Normal University College of Computer and Cyber Security Shijiazhuang050024 China Hebei Provincial Key Laboratory of Network & Information Security Shijiazhuang China Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics & Data Security Shijiazhuang China Hebei Normal University College of China Combustion Engineering Shijiazhuang050024 China

ISBN: (纸本)9798350337754

In the era of big data, high-performance rolling bearing fault diagnostic methods are of great significance to ensure manufacturing smoothly and avoid mass economic losses. A rolling bearing fault diagnostic CAR-SNN model based on Convolutional Block Attention Module (CBAM), Residual network (ResNet), and Spiking Neural networks (SNNs) is proposed based on deep learning (DL). The CAR-SNN model combines CBAM, ResNet, and SNNs to extract critical features while avoiding gradient vanishing or explosion phenomena. The ability of the CAR-SNN model to process spatio-temporal information is improved by using SNNs that incorporate spatio-temporal information simultaneously. The proposed model is used for the rolling bearing fault diagnosis, which datasets are from the experimental platforms of CWRU and Jiangnan University, with the highest diagnostic accuracy 99.995% and 99.18%, respectively. The experimental results validate the effectiveness and stability of the proposed model. The results of comparison experiments show that the CAR-SNN model has better fault diagnostic accuracy and stability than that of the CAR model and other comparison methods. The proposed data-driven rolling bearing fault diagnostic method based on the CAR-SNN model is easy to be implemented, and meeting the needs of high fault diagnostic accuracy in practical applications, and provides valuable input for fault diagnosis of rolling bearings. © 2023 IEEE.

关键词： Failure analysis

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Block-Streaming Service Loading Optimization of Android Dalvik Executable File for Cloud-End Collaboration

Block-Streaming Service Loading Optimization of Android Dalv...

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2022 International Conference on networking and network Applications, NaNA 2022

作者： Wang, Yichuan Feng, Yanhua Du, Yanning Hei, Xinhong Tian, Yuying Cui, Xiangjie School of Computer Science and Engineering Xi’an University of Technology Xi’an China Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an China

ISBN: (数字)9781665461313

ISBN: (纸本)9781665461313

In recent years, dynamic loading technologies of Android applications have emerged in an endless stream. At present, the existing Android application loading method is to load the entire Android Dalvik Executable (DEX) file for cloud-end collaboration, and there is a problem of finer-grained loading optimization in the loading process. Based on the analysis of Android DEX file in this paper, a formal model of each class is established by using the analysis result of this file, analyzes the user's behavior, and tries to ensure that the class to be used in high probability is loaded into memory first, and a block-streaming service loading optimization model of Android DEX file is put forward. The validity of the model is verified by quantitative analysis of Android DEX file. Trying to load the whole Dalvik Executable file and the classes needed by the application separately, recording and comparing the storage space and loading time overhead. In order to avoid the uncertain factors of the network environment, the above files have been tested and recorded several times. The result shows that the time and storage space for loading the whole DEX file are far greater than the sum of the classes required by the application. With this model, the fine-grained formal analysis of Android DEX file can be performed, which is of great significance for exploring the loading method of application block flow service. © 2022 IEEE.

关键词： Android (operating system)

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Automatic skin lesion classification using a novel densely connected convolutional network integrated with an attention module

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Journal of Ambient Intelligence and Humanized Computing 2023年第7期14卷 8943-8956页

作者： Shan, Pufang Chen, Jialei Fu, Chong Cao, Lin Tie, Ming Sham, Chiu-Wing School of Computer Science and Engineering Northeastern University Shenyang110819 China Engineering Research Center of Security Technology of Complex Network System Ministry of Education Shenyang China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang110819 China School of Information and Communication Engineering Beijing Information Science and Technology University Beijing100101 China Science and Technology on Space Physics Laboratory Beijing100076 China School of Computer Science The University of Auckland Auckland New Zealand

Automated skin lesion classification in dermoscopy images remains challenging due to the existence of artefacts and intrinsic cutaneous features, diversity of lesion morphology, insufficiency of training data, and class imbalance problem. To address these challenges, we propose a new densely connected convolutional network termed AttDenseNet-121, which is obtained by integrating the convolutional block attention module (CBAM) into DenseNet-121. CBAM is a simple yet efficient attention module, which is further improved by adding a novel pooling layer (multiscale-pooling) to effectively enhance its attention capability. The optimized CBAM strengthens the representation power of DenseNet-121 by emphasizing meaningful features and suppressing unnecessary ones, thus significantly enhancing the classification performance. Besides, to handle the imbalanced dataset, we employ an improved focal loss function rather than the traditional cross-entropy loss function to train AttDenseNet-121. The improved focal loss is calculated from a different perspective compared with the original focal loss. It makes the distribution of positive samples and negative samples in each batch the same as that in the original data, successfully mitigating the negative influence of class imbalance on this multi-class classification task. We conduct extensive experiments on the public benchmark dataset (HAM10000) and the results indicate that our proposed method achieves superior performance compared with that of the baselines and state-of-the-art algorithms. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Convolution

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GA-Net: Gated Attention Mechanism Based Global Refinement network for Image Inpainting 29

GA-Net: Gated Attention Mechanism Based Global Refinement Ne...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Qiu, Ran Zhao, Shengrong Liang, Hu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350330717

Underwater images are often affected by problems such as light attenuation, color distortion, noise and scattering, resulting in image defects. A novel image inpainting method is proposed to intelligently predict and fill damaged areas for complete and continuous visualization of the image. First, in order to effectively solve the problem of color distortion caused by light refraction in underwater environments, the improved gated attention mechanism is used. This mechanism improves the local details by learning and weighting the important features of the image. Second, gated convolution automatically determines the degree of restoration for each pixel based on local features of the original image. It eliminates distractions such as low contrast and scattering, retaining more original detailed information. By doing so, image inpainting techniques improve the quality and visualization of underwater images. © 2023 IEEE.

关键词： Gated convolution Image inpainting Underwater images

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Multilingual Knowledge Graph Completion via Multimodal Decouple and Relation-based Ensemble Inference 29

Multilingual Knowledge Graph Completion via Multimodal Decou...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Liu, Xiaojing Jiang, Xuesong Yi, Fengge Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350330717

The objective of Multimodal Knowledge Graph Completion (MKGC) is to forecast absent entities within a knowledge graph by leveraging additional textual and visual modalities. Existing studies commonly utilize a singular relationship embedding to depict all modalities within an entity pair, thus connecting several relationships derived from diverse modalities. However, this coupling may introduce interference from conflicting information between modalities, as the relationships between modalities for a given entity pair can be contradictory. Moreover, existing Ensemble Inference methods fail to dynamically adjust modal weights based on their differences and importance, despite the varying contributions of different modalities. In this paper, we propose the Multimodal Decouple and Relation-based Ensemble inference (MDRE) model. For each modality, we construct corresponding relationship embeddings and build separate triple representations to avoid interferences among modalities. During the training phase, we employ confidence-constrained training with temperature scaling to alleviate conflicting information in textual and visual modalities. For inference, we utilize the Relation-based Ensemble Inference method to adjust modal weights at the relationship level, thus achieving improved prediction results. Experimental results on two datasets demonstrate that MDRE outperforms existing single-modal and multimodal knowledge graph completion methods in terms of performance. © 2023 IEEE.

关键词： knowledge graph knowledge graph completion multimodal fusion

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Efficient Wildcard Searchable Symmetric Encryption with Forward and Backward security 1st

Efficient Wildcard Searchable Symmetric Encryption with For...

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1st International Conference on Artificial Intelligence security and Privacy, AIS and P 2023

作者： Zhang, Xi Su, Ye Wei, Zhongkai Shen, Wenting Qin, Jing College of Computer and Cyber Security Hebei Normal University Shijiazhuang050024 China Hebei Provincial Key Laboratory of Network and Information Security Hebei Normal University Shijiazhuang050024 China School of Information Science and Engineering Shandong Normal University Jinan250358 China School of Mathematics Shandong University Jinan250100 China College of Computer Science and Technology Qingdao University Qingdao266071 China

ISBN: (纸本)9789819997848

Wildcard Searchable Symmetric Encryption can achieve flexibility and pattern matching while protecting data privacy. There is a promising future for combining secure wildcard search with emerging technologies such as AI. However, there are challenges in reducing the communication cost and improving security. In this paper, we propose an efficient wildcard searchable symmetric encryption with forward and backward security. The complexity of communication costs in the search protocol is O(1), independent of the number of characters of wildcard keywords, or files in the search result. It is achieved by a double-compressed index in which the character set and file identifiers are encoded simultaneously. Then, the double-compressed index provides a possibility to achieve oblivious keyword query and update, so that the proposed scheme only reveals the query type (search or update) without anything else. It achieves forward and backward security using the distributed multi-point function and an additively homomorphic symmetric encryption scheme. Detailed security proof and the theoretical comparison show the improvement in security and efficiency. The acceptable overheads of the proposed scheme are presented by the extensive performance evaluation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Pattern matching

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