Financial Data Security Management Method and Edge Computing Platform Based on Intelligent Edge Computing and Big Data

2021 ◽  
pp. 1-9
Author(s):  
Yanni Luo
Keyword(s):  
Big Data ◽  
Data Security ◽  
Security Management ◽  
Edge Computing ◽  
Financial Data ◽  
Computing Platform ◽  
Management Method

scholarly journals Strategies to Implement Edge Computing in a P2P Pervasive Grid

2018 ◽  
Vol 11 (1) ◽  
pp. 1-15
Author(s):  
Luiz Angelo Steffenel ◽  
Manuele Kirsch Pinheiro ◽  
Lucas Vaz Peres ◽  
Damaris Kirsch Pinheiro
Keyword(s):  
Big Data ◽  
Context Awareness ◽  
Edge Computing ◽  
Computing Platform ◽  
Geophysical Application ◽  
Two Factors ◽  
Computing Platforms ◽  
Distributed Computing Platform ◽  
Big Data Computing ◽  
Analyze Data

The exponential dissemination of proximity computing devices (smartphones, tablets, nanocomputers, etc.) raises important questions on how to transmit, store and analyze data in networks integrating those devices. New approaches like edge computing aim at delegating part of the work to devices in the “edge” of the network. In this article, the focus is on the use of pervasive grids to implement edge computing and leverage such challenges, especially the strategies to ensure data proximity and context awareness, two factors that impact the performance of big data analyses in distributed systems. This article discusses the limitations of traditional big data computing platforms and introduces the principles and challenges to implement edge computing over pervasive grids. Finally, using CloudFIT, a distributed computing platform, the authors illustrate the deployment of a real geophysical application on a pervasive network.

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.

Big Data Security Management

2015 ◽  
pp. 53-66
Author(s):  
Zaiyong Tang ◽  
Youqin Pan
Keyword(s):  
Big Data ◽  
Data Security ◽  
Scale Up ◽  
Big Data Analytics ◽  
Security Management ◽  
Future Research ◽  
Maturity Model ◽  
Research Issues ◽  
Security Standards ◽  
Industry Trends

Big data is a buzzword today, and security of big data is a big concern. Traditional security standards and technologies cannot scale up to deliver reliable and effective security solutions in the big data environment. This chapter covers big data security management from concepts to real-world issues. By identifying and laying out the major challenges, industry trends, legal and regulatory environments, security principles, security management frameworks, security maturity model, big data analytics in solving security problems, current research results, and future research issues, this chapter provides researchers and practitioners with a timely reference and guidance in securing big data processing, management, and applications.

scholarly journals Strategies to Implement Edge Computing in a P2P Pervasive Grid

Fog Computing ◽  
2018 ◽  
pp. 142-157 ◽  
Author(s):  
Luiz Angelo Steffenel ◽  
Manuele Kirsch Pinheiro ◽  
Lucas Vaz Peres ◽  
Damaris Kirsch Pinheiro
Keyword(s):  
Big Data ◽  
Context Awareness ◽  
Edge Computing ◽  
Computing Platform ◽  
Data Analyses ◽  
Geophysical Application ◽  
Two Factors ◽  
Computing Platforms ◽  
Distributed Computing Platform ◽  
Big Data Computing

The exponential dissemination of proximity computing devices (smartphones, tablets, nanocomputers, etc.) raises important questions on how to transmit, store and analyze data in networks integrating those devices. New approaches like edge computing aim at delegating part of the work to devices in the “edge” of the network. In this article, the focus is on the use of pervasive grids to implement edge computing and leverage such challenges, especially the strategies to ensure data proximity and context awareness, two factors that impact the performance of big data analyses in distributed systems. This article discusses the limitations of traditional big data computing platforms and introduces the principles and challenges to implement edge computing over pervasive grids. Finally, using CloudFIT, a distributed computing platform, the authors illustrate the deployment of a real geophysical application on a pervasive network.

Big Data Security Management

Big Data ◽  
2016 ◽  
pp. 247-260
Author(s):  
Zaiyong Tang ◽  
Youqin Pan
Keyword(s):  
Big Data ◽  
Data Security ◽  
Scale Up ◽  
Big Data Analytics ◽  
Security Management ◽  
Future Research ◽  
Maturity Model ◽  
Research Issues ◽  
Security Standards ◽  
Industry Trends

Big data is a buzzword today, and security of big data is a big concern. Traditional security standards and technologies cannot scale up to deliver reliable and effective security solutions in the big data environment. This chapter covers big data security management from concepts to real-world issues. By identifying and laying out the major challenges, industry trends, legal and regulatory environments, security principles, security management frameworks, security maturity model, big data analytics in solving security problems, current research results, and future research issues, this chapter provides researchers and practitioners with a timely reference and guidance in securing big data processing, management, and applications.

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