IEEE Transactions on Cloud Computing最新文献_第4页

Reversible Data Hiding in Encrypted Images Based on Chinese Remainder Theorem 基于中国剩余定理的加密图像可逆数据隐藏

IF 5 2区计算机科学

IEEE Transactions on Cloud Computing Pub Date : 2025-03-14 DOI: 10.1109/TCC.2025.3570327

Jiani Chen;Dawen Xu

{"title":"Reversible Data Hiding in Encrypted Images Based on Chinese Remainder Theorem","authors":"Jiani Chen;Dawen Xu","doi":"10.1109/TCC.2025.3570327","DOIUrl":"https://doi.org/10.1109/TCC.2025.3570327","url":null,"abstract":"To deal with the development of the distributed server, this article proposes a new method for reversible data hiding in encrypted images based on the Chinese Remainder Theorem (CRT), encrypting and sharing one image to multiple data hiders through <inline-formula><tex-math>$(k,n)$</tex-math></inline-formula>-threshold secret sharing. First, an original image is divided into the most significant bit (MSB) compression area and the least significant bit (LSB) area by utilizing the spatial correlation. The <inline-formula><tex-math>$l$</tex-math></inline-formula>-MSB layers are predicted to obtain prediction errors, and these prediction errors are compressed by Huffman coding. Then according to the value of <inline-formula><tex-math>$k$</tex-math></inline-formula>, CRT and secret sharing scheme are performed on the <inline-formula><tex-math>$(8-l)$</tex-math></inline-formula>-LSB layers to generate the shared bitstream. Finally, <inline-formula><tex-math>$n$</tex-math></inline-formula> encrypted images for sharing consist of MSB compression bitstreams and shared bitstreams, whose size is adjusted based on <inline-formula><tex-math>$k$</tex-math></inline-formula> value. Each data hider can independently embed secret data after having one of the encrypted images, while the receiver can recover the original image only after receiving <inline-formula><tex-math>$k$</tex-math></inline-formula> or more encrypted images. Experimental results show that the proposed algorithm not only provides a large embedding space for secret data, but is also able to complete the inverse operation of data hiding and realize the lossless recovery of the original image with <inline-formula><tex-math>$(k,n)$</tex-math></inline-formula>-threshold secret sharing.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"13 3","pages":"821-836"},"PeriodicalIF":5.0,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998242","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Consortium Blockchain-Based Federated Sensor-Cloud for IoT Services 联盟基于区块链的物联网服务联邦传感器云

IF 5.3 2区计算机科学

IEEE Transactions on Cloud Computing Pub Date : 2025-03-10 DOI: 10.1109/TCC.2025.3543627

Sudip Misra;Aishwariya Chakraborty;Ayan Mondal;Dhanush Kamath

{"title":"Consortium Blockchain-Based Federated Sensor-Cloud for IoT Services","authors":"Sudip Misra;Aishwariya Chakraborty;Ayan Mondal;Dhanush Kamath","doi":"10.1109/TCC.2025.3543627","DOIUrl":"https://doi.org/10.1109/TCC.2025.3543627","url":null,"abstract":"This work addresses the problem of ensuring service availability, trust, and profitability in sensor-cloud architecture designed to <italic>Sensors-as-a-Service (Se-aaS) using IoT generated data. Due to the requirement of geographically distributed wireless sensor networks for Se-aaS, it is not always possible for a single Sensor-cloud Service Provider (SCSP) to meet the end-users requirements. To address this problem, we propose a federated sensor-cloud architecture involving multiple SCSPs for provisioning high-quality Se-aaS. Moreover, for ensuring trust in such a distributed architecture, we propose the use of <italic>consortium blockchain to keep track of the activities of each SCSP and to automate several functionalities through <italic>Smart Contracts. Additionally, to ensure profitability and end-user satisfaction, we propose a composite scheme, named BRAIN, comprising of two parts. First, we define <italic>miner's score to select an optimal subset of SCSPs as <italic>miners periodically. Second, we propose a modified <italic>multiple-leaders-multiple-followers Stackelberg game-theoretic approach to decide the association of an optimal subset of SCSPs to each service. Thereafter, we evaluate the performance of BRAIN by comparing with three existing benchmark schemes through simulations. Simulation results depict that BRAIN outperforms existing schemes in terms of profits and resource consumption of SCSPs, and price charged from end-users.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"13 2","pages":"605-616"},"PeriodicalIF":5.3,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144232039","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Demand-Aware Distributed Scheduling With Adaptive Buffer Control in Reconfigurable Data Center Networks 基于自适应缓冲控制的可重构数据中心网络需求感知分布式调度

IF 5 2区计算机科学

IEEE Transactions on Cloud Computing Pub Date : 2025-03-09 DOI: 10.1109/TCC.2025.3568369

Subin Han;Eunsok Lee;Hyunkyung Yoo;Namseok Ko;Sangheon Pack

{"title":"Demand-Aware Distributed Scheduling With Adaptive Buffer Control in Reconfigurable Data Center Networks","authors":"Subin Han;Eunsok Lee;Hyunkyung Yoo;Namseok Ko;Sangheon Pack","doi":"10.1109/TCC.2025.3568369","DOIUrl":"https://doi.org/10.1109/TCC.2025.3568369","url":null,"abstract":"Reconfigurable data center networks (RDCNs), integrating the electrical packet switch (EPS) with the optical circuit switch (OCS), improve network adaptability by enabling high-throughput connections between top-of-rack (ToR) pairs. However, existing RDCN scheduling schemes face challenges in responsiveness, particularly during traffic bursts. In this article, we propose a novel demand-aware distributed scheduling framework called P4-DADS, utilizing P4-based programmable ToR switches (P4ToR). To prevent conflicts arising from simultaneous OCS port allocations, P4-DADS employs a token-ring-based distributed reservation algorithm, enhanced with an adaptive buffer control (ABC) mechanism. By formulating a Markov decision process (MDP) problem, the optimal ABC policy is obtained through a value iteration algorithm, ensuring that packets are immediately ready for transmission during sudden demand surges. P4-DADS improves network responsiveness and scalability, as evidenced by a 145.95% increase in throughput and a 87.31% reduction in flow completion time. These improvements demonstrate the potential of P4-DADS as a scalable and efficient solution for resource management in RDCN.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"13 3","pages":"783-793"},"PeriodicalIF":5.0,"publicationDate":"2025-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Achieving Enhanced Bi-Linear Attention Network for Teaching Manner Analysis Over Edge Cloud-Assisted AIoT: Voice-Body Coordination Perspective 在边缘云辅助AIoT上实现教学方式分析的增强双线性注意网络：声音-身体协调视角

IF 5 2区计算机科学

IEEE Transactions on Cloud Computing Pub Date : 2025-03-08 DOI: 10.1109/TCC.2025.3568394

Yu Zhou;Sai Zou;Bochun Wu;Wei Ni;Xiaojiang Du

{"title":"Achieving Enhanced Bi-Linear Attention Network for Teaching Manner Analysis Over Edge Cloud-Assisted AIoT: Voice-Body Coordination Perspective","authors":"Yu Zhou;Sai Zou;Bochun Wu;Wei Ni;Xiaojiang Du","doi":"10.1109/TCC.2025.3568394","DOIUrl":"https://doi.org/10.1109/TCC.2025.3568394","url":null,"abstract":"Edge computing, an advanced extension of cloud computing, provides superior computational capabilities and low-latency processing at the network edge, facilitating its availability for real-time data analysis in resource-limited settings. When applied to the analysis of teaching methodologies, edge computing enables the seamless integration of vocal and physical cues, facilitating collaborative, dynamic, and real-time evaluations of teaching quality. However, the inherent complexity of human perception and multimodal interactions impose great challenges to the analysis of these aspects in Artificial Intelligence of Things (AIoT). This paper introduces an innovative mathematical model and a measurement index specifically designed to assess changes in voice-body coordination over time. To achieve this, we propose a cloud-enabled enhanced Bi-Linear Attention Network incorporating entropy and Fourier transforms (BAN-E-FT), which leverages both temporal and frequency-domain features. Specifically, by harnessing the computational and storage capabilities of edge computing, BAN-E-FT facilitates distributed training, expedites large-scale data processing, and enhances model scalability, where entropy measures and Fourier transforms capture modality dynamics, enhancing BAN's fusion capabilities. Moreover, a conditional domain adversarial network is embedded to address regional teaching variations, improving model generalizability. We also verify the robustness of BAN-E-FT with accuracy and convergence through convex optimization analysis. Experiments on the eNTERFACE’05 dataset demonstrate 81% accuracy in assessing teaching adaptability, while real-world test at Guizhou University confirms 78% accuracy when using BAN-E-FT, matching human expert assessments.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"13 3","pages":"769-782"},"PeriodicalIF":5.0,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

2024 Reviewers List* 2024审稿人名单*

IF 5.3 2区计算机科学

IEEE Transactions on Cloud Computing Pub Date : 2025-03-07 DOI: 10.1109/TCC.2025.3528086

引用次数: 0

Secure and Efficient Cloud-Based Multi-Party Private Set Intersection With Union Protocol 基于联合协议的安全高效的云多方私有集交集

IF 5.3 2区计算机科学

IEEE Transactions on Cloud Computing Pub Date : 2025-03-06 DOI: 10.1109/TCC.2025.3548570

Qian Liu;Yu Zhan;Baocang Wang

{"title":"Secure and Efficient Cloud-Based Multi-Party Private Set Intersection With Union Protocol","authors":"Qian Liu;Yu Zhan;Baocang Wang","doi":"10.1109/TCC.2025.3548570","DOIUrl":"https://doi.org/10.1109/TCC.2025.3548570","url":null,"abstract":"Secure Multi-party Computation (MPC) is a highly active research field, with Private Set Intersection (PSI) being a classic subtopic within it. However, simple intersection computation is insufficient for many real-world scenarios, leading to the development of various PSI variant protocols. In this context, we propose a cloud-based multi-party private set intersection with union protocol, denoted as MPSI-U. This protocol securely computes the intersection of the designated party's set with the union of the sets of all other parties, which can be applied to scenarios such as contact tracing. MPSI-U leverages cloud servers to alleviate the computational burden placed on users, while guaranteeing privacy and security simultaneously for all involved parties with the threshold BGN cryptographic system. Furthermore, a comprehensive formal security analysis of the protocol was conducted under the semi-honest model to prove its resilience against potential security threats. Based on our performance analysis, MPSI-U exhibits favorable characteristics in terms of communication and computation overhead. This enhances the versatility of MPSI-U, rendering it a valuable solution that can be widely applied across various domains and scenarios.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"13 2","pages":"578-589"},"PeriodicalIF":5.3,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144229456","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deadline-Aware Online Job Scheduling for Distributed Training in Heterogeneous Clusters 异构集群分布式训练的截止日期感知在线作业调度

IF 5.3 2区计算机科学

IEEE Transactions on Cloud Computing Pub Date : 2025-03-06 DOI: 10.1109/TCC.2025.3548604

Yuchen Zhang;Long Luo;Gang Sun;Hongfang Yu;Bo Li

{"title":"Deadline-Aware Online Job Scheduling for Distributed Training in Heterogeneous Clusters","authors":"Yuchen Zhang;Long Luo;Gang Sun;Hongfang Yu;Bo Li","doi":"10.1109/TCC.2025.3548604","DOIUrl":"https://doi.org/10.1109/TCC.2025.3548604","url":null,"abstract":"The explosive growth in training data and model sizes has spurred the adoption of distributed deep learning (DL) in heterogeneous computing clusters. Efficiently scheduling distributed training jobs in such heterogeneous environments while ensuring they meet user-specified deadlines remains a critical challenge. While most existing works focus on reducing job completion time in homogeneous clusters, they pay little attention to meeting job deadlines in heterogeneous clusters. To address this issue, we propose <sc>Dancer (Deadline-Aware dyNamiC GPU allocation approach for Efficient Resource utilization), a novel framework that dynamically adjusts not only the number but the type of GPUs assigned to each job throughout its training lifecycle. <sc>Dancer aims to maximize the number of jobs meeting their deadlines in heterogeneous GPU clusters. It decouples job placement from resource allocation and formulates the scheduling optimization problem for maximizing the number of deadline-meeting jobs as an Integer Linear Programming (ILP) problem. To solve this ILP problem in real-time, we propose an online algorithm with a competitive ratio guarantee, leveraging primal-dual and dynamic programming techniques. Extensive trace-driven simulations based on real-world DL workloads demonstrate that <sc>Dancer significantly outperforms state-of-the-art approaches, improving the deadline satisfactory ratio up to 58.9%–74.2%.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"13 2","pages":"590-604"},"PeriodicalIF":5.3,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144232135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Communication Intensive Task Offloading With IDMZ for Secure Industrial Edge Computing 基于IDMZ的安全工业边缘计算通信密集型任务卸载

IF 5.3 2区计算机科学

IEEE Transactions on Cloud Computing Pub Date : 2025-03-05 DOI: 10.1109/TCC.2025.3548043

Yuanjun Laili;Jiabei Gong;Yusheng Kong;Fei Wang;Lei Ren;Lin Zhang

{"title":"Communication Intensive Task Offloading With IDMZ for Secure Industrial Edge Computing","authors":"Yuanjun Laili;Jiabei Gong;Yusheng Kong;Fei Wang;Lei Ren;Lin Zhang","doi":"10.1109/TCC.2025.3548043","DOIUrl":"https://doi.org/10.1109/TCC.2025.3548043","url":null,"abstract":"The Industrial Internet of Things provides an opportunity for flexible and collaborative manufacturing, but introduces more risk and more communication overhead from the Internet to the industrial field. To avoid attacks from unreliable service providers and requesters, Industrial Demilitarized Zone (IDMZ) is introduced in conjunction with firewalls to provide new communication modes between edge servers and industrial devices. As the number of tasks being offloaded to the edge side increases, optimal task offloading to balance the risk and the communication overhead with limited demilitarized buffer size becomes a challenge. Therefore, this paper establishes a mathematical model for secure task offloading in the Industrial Internet-of-Things considering dense communication with different communication modes. Then, a Parallel Gbest-centric differential evolution (P-G-DE) is designed to solve this task offloading problem with a heuristic-embedded initialization strategy, a modified Gbest-centric differential evolutionary operator and a circular-rotated parallelization scheme. The experimental results verify that the proposed method is capable of providing a high-quality solution with a lower risk and a shorter execution time in seconds, compared to six state-of-the-art evolutionary algorithms.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"13 2","pages":"560-577"},"PeriodicalIF":5.3,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144232042","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

PPSKSQ: Towards Efficient and Privacy-Preserving Spatial Keyword Similarity Query in Cloud 基于PPSKSQ的云空间关键字相似度查询

IF 5.3 2区计算机科学

IEEE Transactions on Cloud Computing Pub Date : 2025-03-03 DOI: 10.1109/TCC.2025.3547563

Changrui Wang;Lei Wu;Lijuan Xu;Haojie Yuan;Hao Wang;Wenying Zhang;Weizhi Meng

{"title":"PPSKSQ: Towards Efficient and Privacy-Preserving Spatial Keyword Similarity Query in Cloud","authors":"Changrui Wang;Lei Wu;Lijuan Xu;Haojie Yuan;Hao Wang;Wenying Zhang;Weizhi Meng","doi":"10.1109/TCC.2025.3547563","DOIUrl":"https://doi.org/10.1109/TCC.2025.3547563","url":null,"abstract":"The growth of cloud computing has led to the widespread use of location-based services, such as spatial keyword queries, which return spatial data points within a given range that have the highest similarity in keyword sets to the user’s. As the volume of spatial data increases, providers commonly outsource data to powerful cloud servers. Because cloud servers are untrustworthy, privacy-preserving keyword query schemes have been proposed. However, existing schemes consider only location queries or exact keyword matching. To address these issues, we propose the Privacy-Preserving Spatial Keyword Similarity Query Scheme (PPSKSQ), designed to search for spatial data points with the highest similarity while protecting the privacy of outsourced data, query requests, and results. First, we design two sub-protocols based on improved symmetric homomorphic encryption (iSHE): iSHE-SC for secure size comparison and iSHE-SIP for secure inner product computation. Then, we encode range information and integrate it with a quadtree to construct a novel index structure. Additionally, we use the Jaccard to measure similarity in conjunction with the iSHE-SC protocol, transforming similarity comparison into a matrix trace operation. Finally, rigorous security analysis and extensive simulation experiments confirm the flexibility, efficiency, and scalability of our scheme.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"13 2","pages":"544-559"},"PeriodicalIF":5.3,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144230589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

RLDR: Reinforcement Learning-Based Fast Data Recovery in Cloud-of-Clouds Storage Systems 基于强化学习的云-云存储系统快速数据恢复

IF 5.3 2区计算机科学

IEEE Transactions on Cloud Computing Pub Date : 2025-02-26 DOI: 10.1109/TCC.2025.3546528

Jiajie Shen;Bochun Wu;Maoyi Wang;Sai Zou;Laizhong Cui;Wei Ni

引用次数: 0