Cloud Security

Biometrics ◽  
2017 ◽  
pp. 1506-1521
Author(s):  
Natasha Csicsmann ◽  
Victoria McIntyre ◽  
Patrick Shea ◽  
Syed S. Rizvi
Keyword(s):  
Cloud Computing ◽  
Performance Analysis ◽  
Large Scale ◽  
Research Issues ◽  
Biometric Technology ◽  
Cloud Computing Security ◽  
Security Technologies ◽  
Encryption Schemes ◽  
Computing Platforms ◽  
Cloud Auditing

Strong authentication and encryption schemes help cloud stakeholders in performing the robust and accurate cloud auditing of a potential service provider. All security-related issues and challenges, therefore, need to be addressed before a ubiquitous adoption of cloud computing. In this chapter, the authors provide an overview of existing biometrics-based security technologies and discuss some of the open research issues that need to be addressed for making biometric technology an effective tool for cloud computing security. Finally, this chapter provides a performance analysis on the use of large-scale biometrics-based authentication systems for different cloud computing platforms.

Cloud Security

2015 ◽  
pp. 236-250
Author(s):  
Natasha Csicsmann ◽  
Victoria McIntyre ◽  
Patrick Shea ◽  
Syed S. Rizvi
Keyword(s):  
Cloud Computing ◽  
Performance Analysis ◽  
Large Scale ◽  
Research Issues ◽  
Biometric Technology ◽  
Cloud Computing Security ◽  
Security Technologies ◽  
Encryption Schemes ◽  
Computing Platforms ◽  
Cloud Auditing

Strong authentication and encryption schemes help cloud stakeholders in performing the robust and accurate cloud auditing of a potential service provider. All security-related issues and challenges, therefore, need to be addressed before a ubiquitous adoption of cloud computing. In this chapter, the authors provide an overview of existing biometrics-based security technologies and discuss some of the open research issues that need to be addressed for making biometric technology an effective tool for cloud computing security. Finally, this chapter provides a performance analysis on the use of large-scale biometrics-based authentication systems for different cloud computing platforms.

scholarly journals A Parallel Unmixing-Based Content Retrieval System for Distributed Hyperspectral Imagery Repository on Cloud Computing Platforms

2021 ◽  
Vol 13 (2) ◽  
pp. 176
Author(s):  
Peng Zheng ◽  
Zebin Wu ◽  
Jin Sun ◽  
Yi Zhang ◽  
Yaoqin Zhu ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Large Scale ◽  
Retrieval System ◽  
Hyperspectral Image ◽  
Parallel Implementation ◽  
Remotely Sensed Data ◽  
Web Interfaces ◽  
Content Retrieval ◽  
Service Mode ◽  
Computing Platforms

As the volume of remotely sensed data grows significantly, content-based image retrieval (CBIR) becomes increasingly important, especially for cloud computing platforms that facilitate processing and storing big data in a parallel and distributed way. This paper proposes a novel parallel CBIR system for hyperspectral image (HSI) repository on cloud computing platforms under the guide of unmixed spectral information, i.e., endmembers and their associated fractional abundances, to retrieve hyperspectral scenes. However, existing unmixing methods would suffer extremely high computational burden when extracting meta-data from large-scale HSI data. To address this limitation, we implement a distributed and parallel unmixing method that operates on cloud computing platforms in parallel for accelerating the unmixing processing flow. In addition, we implement a global standard distributed HSI repository equipped with a large spectral library in a software-as-a-service mode, providing users with HSI storage, management, and retrieval services through web interfaces. Furthermore, the parallel implementation of unmixing processing is incorporated into the CBIR system to establish the parallel unmixing-based content retrieval system. The performance of our proposed parallel CBIR system was verified in terms of both unmixing efficiency and accuracy.

Performance Analysis of Mobile, Edge and Cloud Computing Platforms for Distributed Applications

2021 ◽  
pp. 21-45
Author(s):  
Mohammad Alkhalaileh ◽  
Rodrigo N. Calheiros ◽  
Quang Vinh Nguyen ◽  
Bahman Javadi
Keyword(s):  
Cloud Computing ◽  
Performance Analysis ◽  
Distributed Applications ◽  
Computing Platforms

scholarly journals A REVIEW ON ENGERY EFFICIENT STRATEGY FOR TASK ALLOCATION IN CLOUD ENVIRONMENT

2016 ◽  
Vol 15 (9) ◽  
pp. 7035-7040
Author(s):  
Sakshi Grover ◽  
Mr. Navtej Singh Ghumman
Keyword(s):  
Energy Efficiency ◽  
Cloud Computing ◽  
Resource Provisioning ◽  
Research Issues ◽  
Efficient Management ◽  
Dynamic Resource Provisioning ◽  
Computing Platforms ◽  
And Performance ◽  
Cloud Applications ◽  
Allocation Algorithms

Although cloud computing is now becoming more advanced and matured as many companies have released their own computing platforms to provide services to public, but the research on cloud computing is still in its infancy. Apart from many other challenges of cloud computing, efficient management of energy is one of the most challenging research issues. In this paper we review the existing algorithm of dynamic resource provisioning and allocation algorithms and holistically work to boost data center energy efficiency and performance. This particular paper purposes a) heterogeneous workload and its implication on data centers energy efficiency b) solving the problem of VM resource scheduling to cloud applications

scholarly journals Contextual Contracts for Component-Based Resource Abstraction in a Cloud of HPC Services

2019 ◽  
Author(s):  
Wagner Al Alam ◽  
Francisco Carvalho Junior
Keyword(s):  
Cloud Computing ◽  
Parallel Computing ◽  
Large Scale ◽  
Matrix Multiplication ◽  
Small Scale ◽  
Computing Systems ◽  
Computing Platform ◽  
Computing Platforms

The efforts to make cloud computing suitable for the requirements of HPC applications have motivated us to design HPC Shelf, a cloud computing platform of services for building and deploying parallel computing systems for large-scale parallel processing. We introduce Alite, the system of contextual contracts of HPC Shelf, aimed at selecting component implementations according to requirements of applications, features of targeting parallel computing platforms (e.g. clusters), QoS (Quality-of-Service) properties and cost restrictions. It is evaluated through a small-scale case study employing a componentbased framework for matrix-multiplication based on the BLAS library.

Cloud Computing Security

2012 ◽  
Vol 4 (1) ◽  
pp. 52-66 ◽  
Author(s):  
Junaid Arshad ◽  
Paul Townend ◽  
Jie Xu ◽  
Wei Jie
Keyword(s):  
Cloud Computing ◽  
Large Scale ◽  
State Of The Art ◽  
Emerging Technology ◽  
The State ◽  
Computing Systems ◽  
Cloud Computing Security ◽  
On Demand ◽  
Cloud Computing Adoption ◽  
Security Challenges

The evolution of modern computing systems has lead to the emergence of Cloud computing. Cloud computing facilitates on-demand establishment of dynamic, large scale, flexible, and highly scalable computing infrastructures. However, as with any other emerging technology, security underpins widespread adoption of Cloud computing. This paper presents the state-of-the-art about Cloud computing along with its different deployment models. The authors also describe various security challenges that can affect an organization’s decision to adopt Cloud computing. Finally, the authors list recommendations to mitigate with these challenges. Such review of state-of-the-art about Cloud computing security can serve as a useful barometer for an organization to make an informed decision about Cloud computing adoption.

scholarly journals Cloud bursting galaxy: federated identity and access management

2019 ◽  
Vol 36 (1) ◽  
pp. 1-9 ◽  
Author(s):  
Vahid Jalili ◽  
Enis Afgan ◽  
James Taylor ◽  
Jeremy Goecks
Keyword(s):  
Cloud Computing ◽  
High Speed ◽  
Large Scale ◽  
Best Practice ◽  
Data Transfer ◽  
Data Access ◽  
Web Security ◽  
Biomedical Data ◽  
Authentication And Authorization ◽  
Computing Platforms

Abstract Motivation Large biomedical datasets, such as those from genomics and imaging, are increasingly being stored on commercial and institutional cloud computing platforms. This is because cloud-scale computing resources, from robust backup to high-speed data transfer to scalable compute and storage, are needed to make these large datasets usable. However, one challenge for large-scale biomedical data on the cloud is providing secure access, especially when datasets are distributed across platforms. While there are open Web protocols for secure authentication and authorization, these protocols are not in wide use in bioinformatics and are difficult to use for even technologically sophisticated users. Results We have developed a generic and extensible approach for securely accessing biomedical datasets distributed across cloud computing platforms. Our approach combines OpenID Connect and OAuth2, best-practice Web protocols for authentication and authorization, together with Galaxy (https://galaxyproject.org), a web-based computational workbench used by thousands of scientists across the world. With our enhanced version of Galaxy, users can access and analyze data distributed across multiple cloud computing providers without any special knowledge of access/authorization protocols. Our approach does not require users to share permanent credentials (e.g. username, password, API key), instead relying on automatically generated temporary tokens that refresh as needed. Our approach is generalizable to most identity providers and cloud computing platforms. To the best of our knowledge, Galaxy is the only computational workbench where users can access biomedical datasets across multiple cloud computing platforms using best-practice Web security approaches and thereby minimize risks of unauthorized data access and credential use. Availability and implementation Freely available for academic and commercial use under the open-source Academic Free License (https://opensource.org/licenses/AFL-3.0) from the following Github repositories: https://github.com/galaxyproject/galaxy and https://github.com/galaxyproject/cloudauthz.

A Comprehensive Review of Access Control Mechanism Based on Attribute Based Encryption Scheme for Cloud Computing

2019 ◽  
Vol 11 (3) ◽  
pp. 33-52
Author(s):  
Lokesh B. Bhajantri ◽  
Tabassum N. Mujawar
Keyword(s):  
Cloud Computing ◽  
Access Control ◽  
Large Scale ◽  
Control Mechanism ◽  
Fine Grained ◽  
Prevailing Paradigm ◽  
Attribute Based Encryption ◽  
Encryption Schemes ◽  
Access Control Mechanism ◽  
A New Technique

Cloud computing is the most prevailing paradigm, which provides computing resources and services over the Internet. Due to immense development in services provided by cloud computing, the trend to share large-scale and confidential data on cloud has been increased. Though cloud computing provides many benefits, ensuring security of the data stored in cloud is the biggest challenge. The security concern about the data becomes main barrier for adoption of cloud. One of the important security aspects is fine grained access control mechanism. The most widely used and efficient access control scheme for cloud computing is Attribute Based Encryption (ABE). The Attribute Based Encryption (ABE) scheme provides a new technique for embedding access policies cryptographically into encryption process. The article presents an overview of various existing attribute-based encryption schemes and traditional access control models. Also, the comparison of existing ABE schemes for cloud computing, on basis of various criteria is presented in the article.

scholarly journals Security Recommendatios And Security Issues Associated With Big Data In Cloud Computing

2020 ◽  
Vol 4 (4) ◽  
pp. 30-37
Author(s):  
Sambaiah G ◽  
Hyma Birudaraju
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Computer Security ◽  
Large Scale ◽  
Focal Point ◽  
Small Scale ◽  
Cloud Computing Security ◽  
Security Issues ◽  
Big Data Applications ◽  
Security Surveillance

In this paper, we discuss security surveillance for Big data, cloud computing, Map Reduce and Hadoop environment. The focal point is on security surveillance in cloud computing that are corresponding with big data. Big data applications are a admirable beneficent to organizations, business, companies and many large scale and small scale industries.We also talk about various attainable solutions for the issues in Hadoop and cloud computing security. Cloud computing security is being blossom at a rapid pace which incorporate with computer security, network security, information security, and data privacy.Cloud computing plays a very crucial role in keep safe data, applications and the related infrastructure with the help of technologies, policies,controls , and big data tools. Moreover, Cloud computing, big data and its applications, advantages are likely to illustrated the most hopeful new boundaries in science.

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