A framework for critical security factors that influence the decision of cloud adoption by Saudi government agencies

Alassafi, Madini O.; Alharthi, Abdulrahman; Walters, Robert John; Wills, Gary

doi:10.1016/j.tele.2017.04.010

Cited by 36 publications

(29 citation statements)

References 30 publications

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“…With the advancement in cloud computing, there has been numerous existing works considering the problems confronting the security of the cloud architecture [36], [37], cloud storage [38]- [40], data mining [30], [41], authentication of protocols [53], security robustness [54], flexible access control of sharing files [55]and privacy-preserving model [7], [42] of the deep learning model and the training dataset in the cloud. Existing works on privacy-preserving cryptographic schemes in the cloud are basically based on secure multi-party computations.…”

Section: A Privacy-preserving Deep Learningmentioning

confidence: 99%

MSCryptoNet: Multi-Scheme Privacy-Preserving Deep Learning in Cloud Computing

2019

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Privacy in the Internet of Things is a fundamental challenge for the Ubiquitous healthcare systems that depend on the data aggregated and collaborative deep learning among different parties. This paper proposes the MSCryptoNet, a novel framework that enables the scalable execution and the conversion of the state-of-the-art learned neural network to the MSCryptoNet models in the privacy-preservation setting. We also design a method for approximation of the activation function basically used in the convolutional neural network (i.e., Sigmoid and Rectified linear unit) with low degree polynomials, which is vital for computations in the homomorphic encryption schemes. Our model seems to target the following scenarios: 1) the practical way to enforce the evaluation of classifier whose inputs are encrypted with possibly different encryption schemes or even different keys while securing all operations including intermediate results and 2) the minimization of the communication and computational cost of the data providers. The MSCryptoNet is based on the multi-scheme fully homomorphic encryption. We also prove that the MSCryptoNet as a privacy-preserving deep learning scheme over the aggregated encrypted data is secured.

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Section: A Privacy-preserving Deep Learningmentioning

confidence: 99%

MSCryptoNet: Multi-Scheme Privacy-Preserving Deep Learning in Cloud Computing

2019

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“…Conversely, customization of those services regarding software testing has been largely ignored in the preceding literature. What's more, few studies focus on Cloud adoption [42][43][44][45][46][47][48] but they are not conducted form software testing, the only exception are [22,25]. A recent survey on testing in the Cloud [49] identified lack of a Cloud testing adoption model that guides decision-maker, when and how to move testing to the Cloud, based on various decision factors as a research gap.…”

Section: Background and Motivationmentioning

confidence: 99%

Fuzzy Multicriteria Decision-Making Approach for Measuring the Possibility of Cloud Adoption for Software Testing

Ali

Ullah²,

Abrar

et al. 2020

Scientific Programming

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To reduce costs and improve organizational efficiency, the adoption of innovative services such as Cloud services is the current trend in today’s highly competitive global business venture. The aim of the study is to guide the software development organization (SDO) for Cloud-based testing (CBT) adoption. To achieve the aim, this study first explores the determinants and predictors of Cloud adoption for software testing. Grounded on the collected data, this study designs a technology acceptance model using fuzzy multicriteria decision-making (FMCDM) approach. For the stated model development, this study identifies a list of predictors (main criteria) and factors (subcriteria) using systematic literature review (SLR). In the results of SLR, this study identifies seventy subcriteria also known as influential factors (IFs) from a sample of 136 papers. To provide a concise understanding of the facts, this study classifies the identified factors into ten predictors. To verify the SLR results and to rank the factors and predictors, an empirical survey was conducted with ninety-five experts from twenty different countries. The application value in the industrial field and academic achievement of the present study is the development of a general framework incorporating fuzzy set theory for improving MCDM models. The model can be applied to predict organizational Cloud adoption possibility taking various IFs and predictors as assessment criteria. The developed model can be divided into two main parts, ranking and rating. To measure the success or failure contribution of the individual IFs towards successful CBT adoption, the ranking part of the model will be used, while for a complete organizational assessment in order to identify the weak area for possible improvements, the assessment part of the model will be used. Collectively, it can be used as a decision support system to gauge SDO readiness towards successful CBT.

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“…Alassafi et al [4] stated complications regarding the adoption of cloud technology in Saudi government agencies with a proposed security factors framework. The framework consists of security risks factors, security benefits factors, and social security factors that influence the decision to use cloud-based services in Saudi Arabia.…”

Section: Critical Security Factors That Influence the Decision Of Clomentioning

confidence: 99%

Ontology-Based Cyber Security Policy Implementation in Saudi Arabia

Talib¹,

Alomary²,

Alwadi³

et al. 2018

JIS

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Cyber security is an important element of national security and the safekeeping of a nation's constituency and assets. In Saudi Arabia, the point of interest on cyber security is particularly outstanding due to the fact that Saudi Arabia has a highly cyber attacks all over the Arab countries. This paper displays on contemporary studies done in Saudi Arabia in regards to cyber security policy coverage. The point of interest of this paper is the use of ontology to identify and suggest a formal, encoded description of the cyber security strategic environment, and propose the development of ontology to be able to permit the implementation of the sort of policy. The intention of the ontology is to become aware of and constitute the multi-layered company of gamers and their related roles and obligations within the cyber security environment. This could make contributions in large part to the improvement, implementation and rollout of a country wide cyber security policy in Saudi Arabia.

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A framework for critical security factors that influence the decision of cloud adoption by Saudi government agencies

Abstract: Cloud computing technologies can play an essential role in public organisations

Cited by 36 publications

References 30 publications

MSCryptoNet: Multi-Scheme Privacy-Preserving Deep Learning in Cloud Computing

MSCryptoNet: Multi-Scheme Privacy-Preserving Deep Learning in Cloud Computing

Fuzzy Multicriteria Decision-Making Approach for Measuring the Possibility of Cloud Adoption for Software Testing

Ontology-Based Cyber Security Policy Implementation in Saudi Arabia

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