Applying Data Mining Techniques to Improve Information Security in the Cloud: A Single Cache System Approach

Alshawi, Amany

doi:10.1155/2016/2385654

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…Different security and privacy protection techniques were reviewed in [16] from both hardware and software aspects, therefore enhancing the data security. Data mining techniques were applied in [17] using Apriori algorithm to ensure security in cloud. An efficient and provable secure anonymous two factor authentication was designed in [18] for not only ensuring security but was also proved to be computationally efficient.…”

Section: Related Workmentioning

confidence: 99%

Certificateless Bilinear Quantum Mutual Exclusive Signcryption for Data Security in Cloud

K*,

2019

IJITEE

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Signcryption perform both encryption and signature verification simultaneously with minimum computational time and overhead when compared to that of the traditional signature model. Certificateless Sigcryption rectifies issues corresponding to the key escrow problem and hence reducing the key management in the traditional key cryptography in Cloud environment. There has been some Certificateless Signcryption methods proposed, most of which are proved secured using the proxy pairing operations. However, with proxy pairing found to be computationally difficult in understanding and with the discrete operation reducing the advantages gained from smaller key size, data security is said to be compromised. To address this issue, in this work, a method called, Bilinear Quantum Mutual Exclusive Signcryption (BQ-MES) for data security in cloud environment is presented based on quantum principles. The new method inherits the security of bilinear mapping along with quantum, which possesses lower computation complexity than proxy operations, employed in signcrypting data in cloud environment. In the BQ-MES method, only a designated authorized cloud user recovers the data stored in the cloud via cloud service provider by verifying the validity of a signcrypted data. This is performed using Mutually Exclusive Probability model. Experimental works are conducted on the parameters such as computational time, computational overhead and data security rate. By evaluating the performance with related schemes, results show that the data stored in cloud environment is secured using BQ-MES method and computationally efficient.

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Section: Related Workmentioning

confidence: 99%

Certificateless Bilinear Quantum Mutual Exclusive Signcryption for Data Security in Cloud

K*,

2019

IJITEE

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“…In the case of foundations of scientific programming, the work in [173] reviewing the main foundations of scientific programming techniques and the use of pattern matching techniques in large graphs [174] are examples of improvements and works in the scope of software techniques. Finally, applications in coal mining [175], recommendation engines for car sharing services [176], health risk prediction [177], text classification [178], or information security [179] are domains in which data are continuously being generated representing good candidates to apply scientific programming techniques.…”

Section: Data-intensive Engineering Environments and Scientificmentioning

confidence: 99%

Survey of Scientific Programming Techniques for the Management of Data-Intensive Engineering Environments

Álvarez-Rodríguez

Alor-Hernández

Mejia-Miranda

2018

Scientific Programming

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The present paper introduces and reviews existing technology and research works in the field of scientific programming methods and techniques in data-intensive engineering environments. More specifically, this survey aims to collect those relevant approaches that have faced the challenge of delivering more advanced and intelligent methods taking advantage of the existing large datasets. Although existing tools and techniques have demonstrated their ability to manage complex engineering processes for the development and operation of safety-critical systems, there is an emerging need to know how existing computational science methods will behave to manage large amounts of data. That is why, authors review both existing open issues in the context of engineering with special focus on scientific programming techniques and hybrid approaches. 1193 journal papers have been found as the representative in these areas screening 935 to finally make a full review of 122. Afterwards, a comprehensive mapping between techniques and engineering and nonengineering domains has been conducted to classify and perform a meta-analysis of the current state of the art. As the main result of this work, a set of 10 challenges for future data-intensive engineering environments have been outlined.

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Practicality of Data Mining for Proficient Network Security Management

Gounder

Nahar

2018

2018 5th Asia-Pacific World Congress on Computer Science and Engineering (APWC on CSE)

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Applying Data Mining Techniques to Improve Information Security in the Cloud: A Single Cache System Approach

Cited by 3 publications

References 11 publications

Certificateless Bilinear Quantum Mutual Exclusive Signcryption for Data Security in Cloud

Certificateless Bilinear Quantum Mutual Exclusive Signcryption for Data Security in Cloud

Survey of Scientific Programming Techniques for the Management of Data-Intensive Engineering Environments

Practicality of Data Mining for Proficient Network Security Management

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