IEEE Transactions on Cloud Computing最新文献_第2页

Bidirectional Identity-Based Inner-Product Functional Re-Encryption in Vaccine Data Sharing 基于双向身份的疫苗数据共享内产品功能再加密

IF 5.3 2区计算机科学

IEEE Transactions on Cloud Computing Pub Date : 2025-03-19 DOI: 10.1109/TCC.2025.3552740

Jing Wang;Yanwei Zhou;Yasi Zhu;Zhiquan Liu;Bo Yang;Mingwu Zhang

{"title":"Bidirectional Identity-Based Inner-Product Functional Re-Encryption in Vaccine Data Sharing","authors":"Jing Wang;Yanwei Zhou;Yasi Zhu;Zhiquan Liu;Bo Yang;Mingwu Zhang","doi":"10.1109/TCC.2025.3552740","DOIUrl":"https://doi.org/10.1109/TCC.2025.3552740","url":null,"abstract":"With the development of cloud computing, more and more data is stored in cloud servers, which leads to an increasing degree of privacy of data stored in cloud servers. For example, in the critical domain of medical vaccine trials, where public health outcomes hinge on the analysis of sensitive patient data, the imperative to safeguard privacy has never been more pronounced. Traditional encryption methods, though effective at protecting data, often expose vulnerabilities during decryption and lack the ability to support granular data access and computation. One-way re-encryption schemes further impede the agility of data sharing, which is indispensable for the collaborative efforts of research institutions. To address these limitations, we propose a novel bidirectional re-encryption scheme for inner-product functional encryption (IPFE). Our scheme secures data while allowing computation and sharing in an encrypted state, preserving patient privacy without hindering research. By harnessing inner-product functional encryption, our approach allows authorized researchers to extract valuable insights from encrypted data, significantly enhancing privacy protections. Our scheme’s security is predicated on the <inline-formula><tex-math>$l$</tex-math></inline-formula>-ABDHE (augmented bilinear Diffie-Hellman exponent) assumption, ensuring robustness against chosen plaintext attacks within the standard model. This foundation not only secures the data but also yields compact ciphertext length, minimizing storage demands. We introduce a protocol specifically designed for medical vaccine trials, which leverages our bidirectional IB-IPFRE (Identity-Based Inner-Product Functional Re-Encryption) scheme. This protocol enhances data security, supports collaborative research, and maintains patient privacy. Its application in vaccine trials demonstrates the scheme’s effectiveness in protecting sensitive information while enabling critical research insights.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"13 2","pages":"617-628"},"PeriodicalIF":5.3,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144230527","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

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

A Dynamic and Secure Join Query Protocol for Multi-User Environment in Cloud Computing 云计算中多用户环境下动态安全的连接查询协议

IF 5.3 2区计算机科学

IEEE Transactions on Cloud Computing Pub Date : 2025-02-21 DOI: 10.1109/TCC.2025.3544628

Hongjun Li;Debiao He;Qi Feng;Xiaolin Yang;Qingcai Luo

{"title":"A Dynamic and Secure Join Query Protocol for Multi-User Environment in Cloud Computing","authors":"Hongjun Li;Debiao He;Qi Feng;Xiaolin Yang;Qingcai Luo","doi":"10.1109/TCC.2025.3544628","DOIUrl":"https://doi.org/10.1109/TCC.2025.3544628","url":null,"abstract":"The development of cloud computing needs to continuously improve and perfect the privacy-preserving techniques for the user’s confidential data. Multi-user join query, as an important method of data sharing, allows multiple legitimate data users to perform join query over the data owner’s encrypted database. However, some existing join query protocols may face some challenges in the practical application, such as practicality, security, and efficiency. In this article, we put forward a dynamic and secure join query protocol in the multi-user environment. Compared with some existing protocols, the proposed protocol has the following advantages. On the one hand, we utilize the dynamic oblivious cross tags structure to realize an efficient join query with forward and backward security. On the other hand, we combine the randomizable distributed key-homomorphic pseudo-random functions with join query to support multiple data users, which can provide resilience against the single user’s key leakage and resist collusion attacks between the cloud server and a subset of data users. We formally define and prove the security of proposed protocol. In addition, we give a detailed analysis of computation and communication overheads to demonstrate the efficiency of proposed protocol. Finally, we carry out some experimental evaluations to further demonstrate the superiority of functionality and efficiency.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"13 2","pages":"512-525"},"PeriodicalIF":5.3,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144229477","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

HyperDrive: Direct Network Telemetry Storage via Programmable Switches HyperDrive：通过可编程开关的直接网络遥测存储

IF 5.3 2区计算机科学

IEEE Transactions on Cloud Computing Pub Date : 2025-02-18 DOI: 10.1109/TCC.2025.3543477

Ziyuan Liu;Zhixiong Niu;Ran Shu;Wenxue Cheng;Lihua Yuan;Jacob Nelson;Dan R. K. Ports;Peng Cheng;Yongqiang Xiong

{"title":"HyperDrive: Direct Network Telemetry Storage via Programmable Switches","authors":"Ziyuan Liu;Zhixiong Niu;Ran Shu;Wenxue Cheng;Lihua Yuan;Jacob Nelson;Dan R. K. Ports;Peng Cheng;Yongqiang Xiong","doi":"10.1109/TCC.2025.3543477","DOIUrl":"https://doi.org/10.1109/TCC.2025.3543477","url":null,"abstract":"In cloud datacenter operations, telemetry and logs are indispensable, enabling essential services such as network diagnostics, auditing, and knowledge discovery. The escalating scale of data centers, coupled with increased bandwidth and finer-grained telemetry, results in an overwhelming volume of data. This proliferation poses significant storage challenges for telemetry systems. In this article, we introduce HyperDrive, an innovative system designed to efficiently store large volumes of telemetry and logs in data centers using programmable switches. This in-network approach effectively mitigates bandwidth bottlenecks commonly associated with traditional endpoint-based methods. To our knowledge, we are the first to use a programmable switch to directly control storage, bypassing the CPU to achieve the best performance. With merely 21% of a switch’s resources, our HyperDrive implementation showcases remarkable scalability and efficiency. Through rigorous evaluation, it has demonstrated linear scaling capabilities, efficiently managing 12 SSDs on a single server with minimal host overhead. In an eight-server testbed, HyperDrive achieved an impressive throughput of approximately 730 Gbps, underscoring its potential to transform data center telemetry and logging practices.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"13 2","pages":"498-511"},"PeriodicalIF":5.3,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144232040","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