How does cloud computing help businesses to manage big data issues

Kybernetes ◽  
2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ahmad Latifian
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Management ◽  
Data Storage ◽  
Data Privacy ◽  
High Efficiency ◽  
Future Research ◽  
It Services ◽  
Computing Technology ◽  
Content Type

PurposeBig data has posed problems for businesses, the Information Technology (IT) sector and the science community. The problems posed by big data can be effectively addressed using cloud computing and associated distributed computing technology. Cloud computing and big data are two significant past-year problems that allow high-efficiency and competitive computing tools to be delivered as IT services. The paper aims to examine the role of the cloud as a tool for managing big data in various aspects to help businesses.Design/methodology/approachThis paper delivers solutions in the cloud for storing, compressing, analyzing and processing big data. Hence, articles were divided into four categories: articles on big data storage, articles on big data processing, articles on analyzing and finally, articles on data compression in cloud computing. This article is based on a systematic literature review. Also, it is based on a review of 19 published papers on big data.FindingsFrom the results, it can be inferred that cloud computing technology has features that can be useful for big data management. Challenging issues are raised in each section. For example, in storing big data, privacy and security issues are challenging.Research limitations/implicationsThere were limitations to this systematic review. The first limitation is that only English articles were reviewed. Also, articles that matched the keywords were used. Finally, in this review, authoritative articles were reviewed, and slides and tutorials were avoided.Practical implicationsThe research presents new insight into the business value of cloud computing in interfirm collaborations.Originality/valuePrevious research has often examined other aspects of big data in the cloud. This article takes a new approach to the subject. It allows big data researchers to comprehend the various aspects of big data management in the cloud. In addition, setting an agenda for future research saves time and effort for readers searching for topics within big data.

scholarly journals Research on Big data Security privacy Protection based on cloud Computing

2021 ◽  
Vol 2083 (4) ◽  
pp. 042077
Author(s):  
Tongtong Xu ◽  
Lei Shi
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Storage ◽  
Data Security ◽  
Privacy Protection ◽  
Data Privacy ◽  
Cloud Services ◽  
Security Mechanism ◽  
Computing Platform ◽  
Cloud Servers

Abstract Cloud computing is a new way of computing and storage. Users do not need to master professional skills, but can enjoy convenient network services as long as they pay according to their own needs. When we use cloud services, we need to upload data to cloud servers. As the cloud is an open environment, it is easy for attackers to use cloud computing to conduct excessive computational analysis on big data, which is bound to infringe on others’ privacy. In this process, we inevitably face the challenge of data security. How to ensure data privacy security in the cloud environment has become an urgent problem to be solved. This paper studies the big data security privacy protection based on cloud computing platform. This paper starts from two aspects: implicit security mechanism and display security mechanism (encryption mechanism), so as to protect the security privacy of cloud big data platform in data storage and data computing processing.

Cloud Computing Technology and Science

2013 ◽  
Vol 5 (4) ◽  
pp. 1-4 ◽  
Author(s):  
Ching-Hsien Hsu ◽  
Emmanuel Udoh
Keyword(s):  
Cloud Computing ◽  
Heterogeneous Computing ◽  
Future Research ◽  
Detailed Knowledge ◽  
It Services ◽  
Special Issue ◽  
Computing Technology ◽  
Research Papers ◽  
Computing Technologies ◽  
Challenges And Opportunities

The “Cloud” is a natural evolution of distributed computing and of the widespread adaption of virtualization and SOA. In Cloud Computing, IT-related capabilities and resources are provided as services, via the Internet and on-demand, accessible without requiring detailed knowledge of the underlying technology. By taking advantage of virtualized resources, cloud computing presents an attractive means to address the challenges while realizing the potential of ubiquitous IT services. Consequently, computational scientists are turning their attention to emerging cloud computing technology and science. As such, cloud computing has come to the picture seeking solutions for computing and IT services to be efficient and environmentally friendly. This special issue is in response to the increasing convergence of cloud computing technologies and services, while different approaches exist, challenges and opportunities are numerous in this context. The research papers selected for this special issue represent recent progresses in the field, including works on virtualization, big data intelligence, resource management, services computing architectures and modeling, as well as mobile cloud and applications. This special issue includes seven extended version of the selected papers originally presented at the 4th IEEE International Conference on Cloud Computing Technology and Science (IEEE CloudCom 2012), held at Taipei, Taiwan. The papers selected for this issue not only contribute valuable insights and results but also have particular relevance to the emerging and cloud computing technologies. All of them present high quality results for tackling problems arising from the ever-growing cloud computing, heterogeneous computing as well as sustainable computing technologies. We believe that this special issue provides novel ideas and state-of-the-art techniques in the field, and stimulates future research in the emerging and cloud computing community.

scholarly journals Security in Cloud Computing Using Hash Algorithm: A Neural Cloud Data Security Model

2018 ◽  
Vol 12 (6) ◽  
pp. 143 ◽  
Author(s):  
Osama Harfoushi ◽  
Ruba Obiedat
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Storage ◽  
Data Privacy ◽  
Security Model ◽  
Application Development ◽  
Cloud Data ◽  
Computing Services ◽  
Cloud Computing System ◽  
Cloud Computing Services

Cloud computing is the delivery of computing resources over the Internet. Examples include, among others, servers, storage, big data, databases, networking, software, and analytics. Institutes that provide cloud computing services are called providers. Cloud computing services were primarily developed to help IT professionals through application development, big data storage and recovery, website hosting, on-demand software delivery, and analysis of significant data patterns that could compromise a system’s security. Given the widespread availability of cloud computing, many companies have begun to implement the system because it is cost-efficient, reliable, scalable, and can be accessed from anywhere at any time. The most demanding feature of a cloud computing system is its security platform, which uses cryptographic algorithm levels to enhance protection of unauthorized access, modification, and denial of services. For the most part, cloud security uses algorithms to ensure the preservation of big data stored on remote servers. This study proposes a methodology to reduce concerns about data privacy by using cloud computing cryptography algorithms to improve the security of various platforms and to ensure customer satisfaction.

Business view of cloud

2015 ◽  
Vol 38 (6) ◽  
pp. 582-604 ◽  
Author(s):  
Sowmya Karunakaran ◽  
Venkataraghavan Krishnaswamy ◽  
Sundarraj Rangaraja P
Keyword(s):  
Systematic Review ◽  
Cloud Computing ◽  
Review Paper ◽  
Future Research ◽  
Computing Technology ◽  
Business Decisions ◽  
Content Type ◽  
Classification Framework ◽  
Future Work ◽  
Practical Implications

Purpose – This study aims to investigate the decisions related to business aspects of cloud computing and discuss the research density, models/techniques used and identify opportunities for future work. Design/methodology/approach – In this paper, 155 research articles shortlisted through a systematic review were analyzed and a classification framework was developed. Using this framework, the research density is discussed and a detailed review of four widely researched decision themes is provided. Findings – It was found that current research on business aspects is spread across 23 decision themes. The distribution, however, is skewed with 50 per cent pertaining to just four themes, namely, pricing, markets, sourcing and adoption. Simulation appears to be the preferred modeling approach. Decision themes in consumer behavior, sustainability, auditing and culture offer opportunities for future research. Research limitations/implications – The classification framework organizes extant research on applied models and allows researchers to identify potential avenues for application, improvement and development of models to support business decisions. The review is limited to academic articles and does not include industry reports. Practical implications – Practitioners can readily understand various perspectives relevant to a decision theme such as pricing or sourcing, seek and use associated models such as simulation, optimization and game theory to support their decision-making. Originality/value – Most of the extant review paper deal with cloud computing technology. This study is the first systematic review on the models applied to business aspects of cloud computing. This study provides a classification framework and explicitly lists associated decision themes, models/techniques and opportunities.

Big Data Security Framework for Distributed Cloud Data Centers

2017 ◽  
pp. 288-310 ◽  
Author(s):  
Chandu Thota ◽  
Gunasekaran Manogaran ◽  
Daphne Lopez ◽  
Vijayakumar V.
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Storage ◽  
High Efficiency ◽  
Rapid Development ◽  
Data Generation ◽  
Privacy And Security ◽  
Cloud Data ◽  
Efficiency And Effectiveness ◽  
Cloud Data Storage

The rapid development of data generation sources such as digital sensors, networks, and smart devices along with their extensive use is leading to create huge database and coins the term Big Data. Cloud Computing enables computing resources such as hardware, storage space and computing tools to be provided as IT services in a pay-as-you-go fashion with high efficiency and effectiveness. Cloud-based technologies with advantages over traditional platforms are rapidly utilized as potential hosts for big data. However, privacy and security is one of major issue in cloud computing due to its availability with very limited user-side control. This chapter proposes security architecture to prevent and secure the data and application being deployed in cloud environment with big data technology. This chapter discuss the security issues for big data in cloud computing and proposes Meta Cloud Data Storage architecture to protect big data in cloud computing environment.

Big Data Security Framework for Distributed Cloud Data Centers

Web Services ◽  
2019 ◽  
pp. 240-257
Author(s):  
Chandu Thota ◽  
Gunasekaran Manogaran ◽  
Daphne Lopez ◽  
Vijayakumar V.
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Storage ◽  
High Efficiency ◽  
Rapid Development ◽  
Data Generation ◽  
Privacy And Security ◽  
Cloud Data ◽  
Efficiency And Effectiveness ◽  
Cloud Data Storage

The rapid development of data generation sources such as digital sensors, networks, and smart devices along with their extensive use is leading to create huge database and coins the term Big Data. Cloud Computing enables computing resources such as hardware, storage space and computing tools to be provided as IT services in a pay-as-you-go fashion with high efficiency and effectiveness. Cloud-based technologies with advantages over traditional platforms are rapidly utilized as potential hosts for big data. However, privacy and security is one of major issue in cloud computing due to its availability with very limited user-side control. This chapter proposes security architecture to prevent and secure the data and application being deployed in cloud environment with big data technology. This chapter discuss the security issues for big data in cloud computing and proposes Meta Cloud Data Storage architecture to protect big data in cloud computing environment.

Big Data Security Framework for Distributed Cloud Data Centers

2018 ◽  
pp. 589-607 ◽  
Author(s):  
Chandu Thota ◽  
Gunasekaran Manogaran ◽  
Daphne Lopez ◽  
Vijayakumar V.
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Storage ◽  
High Efficiency ◽  
Rapid Development ◽  
Data Generation ◽  
Privacy And Security ◽  
Cloud Data ◽  
Efficiency And Effectiveness ◽  
Cloud Data Storage

The rapid development of data generation sources such as digital sensors, networks, and smart devices along with their extensive use is leading to create huge database and coins the term Big Data. Cloud Computing enables computing resources such as hardware, storage space and computing tools to be provided as IT services in a pay-as-you-go fashion with high efficiency and effectiveness. Cloud-based technologies with advantages over traditional platforms are rapidly utilized as potential hosts for big data. However, privacy and security is one of major issue in cloud computing due to its availability with very limited user-side control. This chapter proposes security architecture to prevent and secure the data and application being deployed in cloud environment with big data technology. This chapter discuss the security issues for big data in cloud computing and proposes Meta Cloud Data Storage architecture to protect big data in cloud computing environment.

Data Security and Data Privacy in Cloud Computing

2014 ◽  
Vol 905 ◽  
pp. 687-692
Author(s):  
Waleed Al-Museelem ◽  
Chun Lin Li
Keyword(s):  
Cloud Computing ◽  
Data Storage ◽  
Data Security ◽  
Data Privacy ◽  
Research Work ◽  
Research Paper ◽  
Security And Privacy ◽  
Future Research ◽  
Successful Implementation ◽  
Privacy And Security

Cloud computing has led to the development of IT to more sophisticated levels by improving the capacity and flexibility of data storage and by providing a scalable computation and processing power which matches the dynamic data requirements. Cloud computing has many benefits which has led to the transfer of many enterprise applications and data to public and hybrid clouds. However, many organizations refer to the protection of privacy and the security of data as the major issues which prevent them from adopting cloud computing. The only way successful implementation of clouds can be achieved is through effective enhancement and management of data security and privacy in clouds. This research paper analyzes the privacy and protection of data in cloud computing through all data lifecycle stages providing an overall perspective of cloud computing while highlighting key security issues and concerns which should be addressed. It also discusses several current solutions and further proposes more solutions which can enhance the privacy and security of data in clouds. Finally, the research paper describes future research work on the protection of data privacy and security in clouds.

scholarly journals Advantages and potential challenges of data management in e-maintenance

2019 ◽  
Vol 25 (3) ◽  
pp. 378-396 ◽  
Author(s):  
Arian Razmi-Farooji ◽  
Hanna Kropsu-Vehkaperä ◽  
Janne Härkönen ◽  
Harri Haapasalo
Keyword(s):  
Data Management ◽  
Design Methodology ◽  
Management Practices ◽  
Future Research ◽  
Conceptual Approach ◽  
Content Type ◽  
Different Types ◽  
Maintenance Systems ◽  
Industry Leader ◽  
Practical Implications

Purpose The purpose of this paper is twofold: first, to understand data management challenges in e-maintenance systems from a holistically viewpoint through summarizing the earlier scattered research in the field, and second, to present a conceptual approach for addressing these challenges in practice. Design/methodology/approach The study is realized as a combination of a literature review and by the means of analyzing the practices on an industry leader in manufacturing and maintenance services. Findings This research provides a general understanding over data management challenges in e-maintenance and summarizes their associated proposed solutions. In addition, this paper lists and exemplifies different types and sources of data which can be collected in e-maintenance, across different organizational levels. Analyzing the data management practices of an e-maintenance industry leader provides a conceptual approach to address identified challenges in practice. Research limitations/implications Since this paper is based on studying the practices of a single company, it might be limited to generalize the results. Future research topics can focus on each of mentioned data management challenges and also validate the applicability of presented model in other companies and industries. Practical implications Understanding the e-maintenance-related challenges helps maintenance managers and other involved stakeholders in e-maintenance systems to better solve the challenges. Originality/value The so-far literature on e-maintenance has been studied with narrow focus to data and data management in e-maintenance appears as one of the less studied topics in the literature. This research paper contributes to e-maintenance by highlighting the deficiencies of the discussion surrounding the perspectives of data management in e-maintenance by studying all common data management challenges and listing different types of data which need to be acquired in e-maintenance systems.

scholarly journals A Comprehensive Survey on Local Differential Privacy

2020 ◽  
Vol 2020 ◽  
pp. 1-29 ◽  
Author(s):  
Xingxing Xiong ◽  
Shubo Liu ◽  
Dan Li ◽  
Zhaohui Cai ◽  
Xiaoguang Niu
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Statistical Learning ◽  
Data Privacy ◽  
Statistical Estimation ◽  
Differential Privacy ◽  
Future Research ◽  
Reference Source ◽  
Complex Data ◽  
Comprehensive Survey

With the advent of the era of big data, privacy issues have been becoming a hot topic in public. Local differential privacy (LDP) is a state-of-the-art privacy preservation technique that allows to perform big data analysis (e.g., statistical estimation, statistical learning, and data mining) while guaranteeing each individual participant’s privacy. In this paper, we present a comprehensive survey of LDP. We first give an overview on the fundamental knowledge of LDP and its frameworks. We then introduce the mainstream privatization mechanisms and methods in detail from the perspective of frequency oracle and give insights into recent studied on private basic statistical estimation (e.g., frequency estimation and mean estimation) and complex statistical estimation (e.g., multivariate distribution estimation and private estimation over complex data) under LDP. Furthermore, we present current research circumstances on LDP including the private statistical learning/inferencing, private statistical data analysis, privacy amplification techniques for LDP, and some application fields under LDP. Finally, we identify future research directions and open challenges for LDP. This survey can serve as a good reference source for the research of LDP to deal with various privacy-related scenarios to be encountered in practice.

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