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IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY 2025年 6卷 3710-3744页

作者： Rahdari, Ahmad Keshavarz, Elham Nowroozi, Ehsan Taheri, Rahim Hajizadeh, Mehrdad Mohammadi, Mohammadreza Sinaei, Sima Bauschert, Thomas Shiraz Univ Sch Elect & Comp Engn Shiraz *** Iran Pishtazan Higher Educ Inst Dept Comp Engn Shiraz *** Iran Univ Greenwich Ctr Sustainable Cyber Secur London SE10 9LS England Univ Portsmouth Ctr Cybercrime & Econ Crime Portsmouth PO1 2UP England Tech Univ Chemnitz Chair Commun Networks D-09111 Chemnitz Germany Smart Automation & Cyber Resilience SACR Dept Digital Syst S-41258 Gothenburg Sweden

The increasing need to process large, high-dimensional datasets and the substantial computational power required have made the use of distributed cloud servers essential. These servers provide cost-effective solutions that make storage and computing accessible to ordinary users. However, they might face significant vulnerabilities, including data leakage, metadata spoofing, insecure programming interfaces, malicious insiders, and denial of service. To gain public trust in distributed computing, addressing concerns related to privacy and security while ensuring high performance and efficiency is crucial. Multiparty computation, differential privacy, trusted execution environments, and federated learning are the four major approaches developed to address these issues. This survey paper reviews and compares these four approaches based on a structured framework, by highlighting recent top-tier research papers published in prestigious journals and conferences. Particular attention is given to progress in federated learning, which trains a model across multiple devices without sharing the actual data, keeping data private and secure. The survey also highlights federated learning techniques, including secure federated learning, by detecting malicious updates and privacy-preserving federated learning via data encryption, data perturbation, and anonymization, as new paradigms for building responsible computing systems. Finally, the survey discusses future research directions for connecting academic innovations with real-world industrial applications.

关键词： cloud computing Security Privacy Surveys Federated learning Reviews Solid modeling Servers Perturbation methods Information integrity distributed cloud computing edge computing privacy-preserving computing federated learning multi-party computation differential privacy trusted execution environments

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Multi-user privacy-preserving image search in distributed cloud computing

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COMPUTERS & ELECTRICAL ENGINEERING 2024年 118卷

作者： Zhang, Zongye Zhou, Fucai Wang, Yan Northeastern Univ Software Coll Shenyang 100819 Peoples R China

cloud-based image search has emerged as a research focus, but security and privacy concerns limit its application. One secure solution is to encrypt the images before outsourcing them to the cloud. Nevertheless, encryption significantly reduces image availability and suffers from some privacy leakages, such as search pattern leakage, access pattern leakage, and similarity leakage. With the exponential growth of images, the distributed cloud architecture is adopted to provide storage and search services for vast amounts of images. However, ensuring multi-user access control in distributed cloud computing remains a challenge. To address these problems, we propose a multi-user privacy-preserving image search scheme in distributed cloud computing environments. First, we propose an SGX-based privacy-preserving distributed image search framework that leverages SGX to protect image similarity against cloud servers. Second, we present an index re-encryption strategy and an access obfuscation strategy to preserve the search pattern and the access pattern. Last, we propose a multi-secret sharing-empowered multi-user access control method enabling multi-user queries and facilitating lightweight multiuser management in distributed environments. Security analysis and experimental evaluation demonstrate that our proposed scheme guarantees privacy with efficient search.

关键词： Encrypted image search Privacy-preserving Searchable encryption distributed cloud computing

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Market-Based Resource Allocation of distributed cloud computing Services: Virtual Energy Storage Systems

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IEEE INTERNET OF THINGS JOURNAL 2022年第22期9卷 22811-22821页

作者： Tao, Yuechuan Qiu, Jing Lai, Shuying Sun, Xianzhuo Zhao, Junhua Univ Sydney Sch Elect & Informat Engn Sydney NSW 2006 Australia Chinese Univ Hong Kong Sch Sci & Engn Shenzhen 518100 Peoples R China Shenzhen Inst Artificial Intelligence & Robot Soc Shenzhen 518172 Peoples R China

The cloud-based application is a major developing feature of smart grids. Apart from centralized Internet data centers (IDCs), distributed cloud resources (CRs) can also provide cloud computing services with low latency and high reliability. For the distributed cloud computing, CRs aggregators (CRAs) will integrate the distributed idle computing resources, which are dispersed energy consumers in the system, to form virtual IDCs. This article presents a market-based computing resource allocation method for distributed cloud computing services. The computing resource allocation refers to the approach to allocating the computing workloads to different CRs. First, the batch workload scheduling (BWS)-based virtual energy storage system (VESS) model and thermal inertia (TI)-based VESS model are proposed to help CRAs better aggregate the distributed CRs and characterize the energy consumption flexibility of the virtual IDCs. Then, the energy trading behavior of the CRAs in the transactive energy market is modeled in the resource allocation process. Case studies are conducted on a 55-bus electricity system. It can be found that energy consumption costs can be reduced by applying the proposed methodology, and revenues from providing cloud computing services can be increased.

关键词： cloud computing Servers Resource management Power demand Mathematical models Computational modeling Aggregates distributed cloud computing Internet data centers (IDC) transactive energy market virtual energy storage

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Application of new digital signal processing technology based on distributed cloud computing in electronic information engineering

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2022年 128卷 443-450页

作者： Sheng, Hu Qi, Xiaodong Dalian Jiaotong Univ Sch Elect & Informat Engn Dalian 116028 Peoples R China

The traditional digital signal processing technology in electronic information engineering has many issues, such as redundant data, low data utilization and so on. To end these issues, this paper proposed a new digital signal processing technology based on distributed cloud computing in electronic information engineering. From the data collection, data analysis, data classification, data mining, effective information storage and other aspects of conventional digital signal, through relying on distributed cloud computing method and intelligent gradient tracking algorithms to achieve efficient processing of digital signal, Proportional Integral Derivative (PID) control strategy is used to evaluate the intelligence degree of each link in the digital signal processing technology. This method can realize the adaptive regulation of data collection and storage in the process of digital signal processing, and realize diversified analysis and intelligent matching. Through the distributed cloud computing to achieve the rapid control of the system storage module, so that the database can improve the work efficiency, reduce the power consumption cost of the system in the process of data operation, and improve the efficiency of digital signal processing. The experimental results show that the digital signal processing system based on distributed cloud computing and intelligent gradient tracking algorithms has the advantages of high computing efficiency, high accuracy and good stability. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Electronic information engineering distributed cloud computing Digital signal processing Proportional integral derivative control

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Video-based Person Re-identification Based on distributed cloud computing

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Journal of Artificial Intelligence and Technology 2021年第2期1卷 110-120页

作者： Chengyan Zhong Xiaoyu Jiang Guanqiu Qi College of Automation Chongqing University of Posts and TelecommunicationsChongqing 400065China College of Intelligence Science and Technology Chongqing University of Posts and TelecommunicationsChongqing 400065China Computer Information Systems Department State University of New York at Buffalo StateBuffaloNY 14222USA

Person re-identification has been a hot research issues in the field of computer *** recent years,with the maturity of the theory,a large number of excellent methods have been ***,large-scale data sets and huge networks make training a time-consuming *** the same time,the parameters and their values generated during the training process also take up a lot of computer ***,we apply distributed cloud computing method to perform person re-identification *** distributed data storage method,pedestrian data sets and parameters are stored in cloud *** speed up operational efficiency and increase fault tolerance,we add data redundancy mechanism to copy and store data blocks to different nodes,and we propose a hash loop optimization algorithm to optimize the data distribution ***,we assign different layers of the re-identification network to different nodes to complete the training in the way of model *** comparing and analyzing the accuracy and operation speed of the distributed model on the video-based dataset MARS,the results show that our distributed model has a faster training speed.

关键词： person re-identification distributed cloud computing data redundancy mechanism

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A distributed Mobile cloud computing Model for Secure Big Data 30

A Distributed Mobile Cloud Computing Model for Secure Big Da...

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30th International Conference on Information Networking (ICOIN)

作者： Sung, Soonhwa Youn, Cheong Kong, Eunbae Ryou, Jaecheol Chungnam Natl Univ Coll Engn Dept Comp Sci & Engn Daejeon 305764 South Korea

ISBN: (纸本)9781509017249

Mobile cloud computing provides a novel e-commerce mode for organizations without any upfront. investment. Since cloud computing uses distributed resources in open environment, it is important to provide secure keys to share the data for developing cloud computing applications. To ensure a correctness of users' data in the cloud, we propose an effective and secure distributed model including a Self-Proxy Server (SPS) with self-created algorithm. The model resolves a communication bottleneck due to re-encryption of a shared data in the cloud whenever users are revoked. It offers to reduce security risks and protect their resources because a distributed SPS dynamically interacts with Key Manager (KM) when the mobile users take on cloud services. This paper presents a comprehensive mobile cloud design which provides an effective and secure cloud computing services on mobile devices.

关键词： mobile cloud computing self-proxy server key manager distributed cloud computing mobile cloud provider

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Data-Driven Matching Protocol for Vehicle-to-Vehicle Energy Management Considering Privacy Preservation

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IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION 2023年第1期9卷 968-980页

作者： Tao, Yuechuan Qiu, Jing Lai, Shuying Sun, Xianzhuo Wang, Yunqi Zhao, Junhua Univ Sydney Sch Elect & Informat Engn Sydney NSW 2006 Australia Monash Univ Dept Data Sci & Artificial Intelligence Clayton Vic 3800 Australia Chinese Univ Hong Kong Sch Sci & Engn Shenzhen 518100 Peoples R China Shenzhen Inst Artificial Intelligence & Robot Soc Shenzhen 518100 Peoples R China

To address the increasing concern on environmental issues, electric vehicles (EVs) have been attracting more attention. On the one hand, the high penetration level of EVs will bring potential risks to the electricity grids. On the other hand, EVs can enhance the flexibility of the grids via vehicle-to-grid (V2G) technology. With the advancement of digitalization and communication technologies, the concept of vehicle-to-vehicle (V2V) has emerged, which can provide an alternative charging way for EVs to relieve the charging overload problem in the power systems. In this article, we present a data-driven matching protocol for V2V energy management. First, deep reinforcement learning (DRL) is utilized to learn the long-term reward of the matching action based on the formulated Markov decision process (MDP) at an offline stage. To protect the private information of the EV owners, a federated learning framework is proposed where the cooperation between different EV aggregators can be realized without sharing sensitive information. At the online matching stage, a matching optimization model is established and is converted into a bipartite graph problem to enhance the computation efficiency. The proposed methodology is verified in case studies. Simulation results show that the proposed methodology can help EV owners save costs and increase revenues while hedging the trading risks. Besides, the local energy self-sufficiency is increased, indicating that less burden is brought to the electricity network.

关键词： distributed cloud computing internet data centers transactive energy market virtual energy storage

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cloud Resource Allocation Recommendation Based on Machine Learning 24

Cloud Resource Allocation Recommendation Based on Machine Le...

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24th International Conference on Transparent Optical Networks (ICTON)

作者： Semwar, Arpit Yue, Xiaofeng Shen, Yuzhe Aibin, Michal Northeastern Univ Khoury Coll Comp Sci Vancouver BC Canada British Columbia Inst Technol Dept Comp Vancouver BC Canada

ISBN: (纸本)9798350377330;9798350377323

R esource allocation is a key topic in distributed cloud systems, especially as more and more cloud products and service offerings introduce decision complexity. It's a common challenge for cloud users to predict usage and budget accordingly. This paper studies the industry practices on smart resource allocation in the cloud and proposes a novel approach using data-driven combination of LSTM and K-means for cloud resource allocation.

关键词： resource allocation scaling distributed cloud computing machine learning deep learning

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Data Gap Filling Using cloud-Based distributed Markov Chain Cellular Automata Framework for Land Use and Land Cover Change Analysis: Inner Mongolia as a Case Study

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REMOTE SENSING 2022年第3期14卷

作者： Lan, Hai Stewart, Kathleen Sha, Zongyao Xie, Yichun Chang, Shujuan Univ Maryland Dept Geog Sci College Pk MD 20742 USA George Mason Univ NSF Spatiotemporal Innovat Ctr Fairfax VA 22030 USA Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan 430079 Peoples R China Eastern Michigan Univ Dept Geog & Geol Ypsilanti MI 48197 USA Inner Mongolia Forestry & Grassland Monitoring & Inner Mongolia Key Lab Remote Sensing Grassland & Hohhot 010020 Peoples R China

With advances in remote sensing, massive amounts of remotely sensed data can be harnessed to support land use/land cover (LULC) change studies over larger scales and longer terms. However, a big challenge is missing data as a result of poor weather conditions and possible sensor malfunctions during image data collection. In this study, cloud-based and open source distributed frameworks that used Apache Spark and Apache Giraph were used to build an integrated infrastructure to fill data gaps within a large-area LULC dataset. Data mining techniques (k-medoids clustering and quadratic discriminant analysis) were applied to facilitate sub-space analyses. Ancillary environmental and socioeconomic conditions were integrated to support localized model training. Multi-temporal transition probability matrices were deployed in a graph-based Markov-cellular automata simulator to fill in missing data. A comprehensive dataset for Inner Mongolia, China, from 2000 to 2016 was used to assess the feasibility, accuracy, and performance of this gap-filling approach. The result is a cloud-based distributed Markov-cellular automata framework that exploits the scalability and high performance of cloud computing while also achieving high accuracy when filling data gaps common in longer-term LULC studies.

关键词： gap filling LULC distributed cloud computing Markov-CA

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A Low-Load distributed Stream Processing System for Continuous Conjunctive Normal Form Queries

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IEEE TRANSACTIONS ON cloud computing 2022年第4期10卷 2281-2293页

作者： Yoshihisa, Tomoki Hara, Takahiro Osaka Univ Cybermedia Ctr Ibaraki Osaka 5670047 Japan Osaka Univ Grad Sch Informat Sci & Technol Suita Osaka 5650871 Japan

One of the research issues for stream data is fast notification of continuous query results. Recent researches assume that query processing servers receive data from remote data sources continuously. Therefore, when a large number of processing servers are distributed in networks, large amounts of communication traffic for those are produced. This is the main cause of delaying query result notifications. To tackle this fundamental problem, we focus on the conjunction of operations and conditions in continuous queries for distributed processing systems. When queries consist of some series of operations and conditions, processing servers can stop query executions if the remaining operations or condition checking are dispensable. Thus, communications less arise and communication traffic is further reduced. Our proposed system represents continuous queries by conjunctive normal forms (CNF queries). The proposed system constructs trigger trees for evaluating the sum terms of CNF queries and determines the timing for their evaluations, in order to reduce communication traffic. We confirmed that one of the proposed methods can reduce the average amount of communication traffic by 23 percent compared with a sensor data serial aggregation method in an evaluation situation.

关键词： Stream data continuous queries distributed cloud computing edge computing

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