• Journal of Convergence for Information Technology
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• v.10 no.10
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• pp.15-23
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• 2020

It is a reality that users do not know well what kind of information protection policy the cloud service provider presents to consumers. The purpose of this study is to find a way to improve the effectiveness of information protection by analyzing the content and linguistic characteristics of information protection policies provided by cloud service providers. In order to achieve the purpose of this study, we investigate the contents of information protection policies of 47 companies that provide cloud services and analyze the influence of linguistic characteristics to come up with a plan to increase the efficiency of cloud services. The research results showed that low readability due to comprehensive expression of technical processing methods, etc., could lead to legal disputes and to hinder the spread of cloud services. The research results can increase the effectiveness of information protection by suggesting items to be provided to users.ing, Privacy, confidentiality, linguistic characteristics, Accounting Information.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.8
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• pp.507-516
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• 2013

Mobile devices have limited computing power and resources. Since mobile devices are equipped with rich network connectivity, an approach to subscribe cloud services can effectively remedy the problem, which is called Mobile Cloud Computing (MCC). Most works on MCC depend on a method to offload functional components at runtime. However, these works only consider the limited verion of offloading to a pre-defined, designated node. Moveover, there is the limitation of managing services subscribed by applications. To provide a comprehensive and practical solution for MCC, in this paper, we propose a self-stabilizing process and its management-related methods. The proposed process is based on an autonomic computing paradigm and works with diverse quality remedy actions such as migration or replicating services. And, we devise a pratical offloading mechanism which is still in an initial stage of the study. The proposed offloading mechanism is based on our proposed MCC meta-model. By adopting the self-stabilization process for MCC, many of the technical issues are effectively resolved, and mobile cloud environments can maintain consistent levels of quality in autonomous manner.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.12
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• pp.4345-4363
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• 2021

Deep Learning as a Service (DLaaS), utilizing the cloud-based deep neural network models to provide customer prediction services, has been widely deployed on mobile cloud computing (MCC). Such services raise privacy concerns since customers need to send private data to untrusted service providers. In this paper, we devote ourselves to building an efficient protocol to classify users' images using the convolutional neural network (CNN) model trained and held by the server, while keeping both parties' data secure. Most previous solutions commonly employ homomorphic encryption schemes based on Ring Learning with Errors (RLWE) hardness or two-party secure computation protocols to achieve it. However, they have limitations on large communication overheads and costs in MCC. To address this issue, we present LeHE4SCNN, a scalable privacy-preserving and communication-efficient framework for CNN-based DLaaS. Firstly, we design a novel low-expansion rate homomorphic encryption scheme with packing and unpacking methods (LeHE). It supports fast homomorphic operations such as vector-matrix multiplication and addition. Then we propose a secure prediction framework for CNN. It employs the LeHE scheme to compute linear layers while exploiting the data shuffling technique to perform non-linear operations. Finally, we implement and evaluate LeHE4SCNN with various CNN models on a real-world dataset. Experimental results demonstrate the effectiveness and superiority of the LeHE4SCNN framework in terms of response time, usage cost, and communication overhead compared to the state-of-the-art methods in the mobile cloud computing environment.

• The KIPS Transactions:PartD
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• v.18D no.6
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• pp.481-492
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• 2011

Recently, various mobile services are provided by the spread of wireless network infrastructures and smart devices. The improvement of cloud computing technologies increases the interests for enterprise mobile cloud services in various IT companies as well. By increasing the interests for enterprise mobile cloud services, it is necessary to evaluate the use of enterprise mobile cloud services. Therefore, the factors which affect the user acceptance of enterprise mobile cloud services are analyzed on the basis of Davis' technology acceptance model in this research. As analysis results, four external variables have significant effects on perceived ease of use of mobile cloud services. Also, these variables indirectly affect attitude toward using cloud services. The results show that the security is the most important factor for attitude toward using enterprise mobile cloud services. The service users also consider the interoperability as an important factor for the user acceptance of cloud services. The perceived ease of use has more contribution than the perceive usefulness on attitude toward using enterprise mobile cloud services. This research has both industrial and academic contributions because it provides the guideline to companies for introducing the enterprise mobile cloud services and apply the technology acceptance model on new IT services.

• Journal of Platform Technology
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• v.12 no.2
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• pp.3-11
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• 2024

In the trend of digital transformation and the increase in non-face-to-face services triggered by the Fourth Industrial Revolution, the demand for cloud computing services is being sharply stimulated. Currently, the cloud is being introduced in various industrial sectors including public, IT, and finance, and the manufacturing industry is also adopting the cloud to secure future sustainability and is promoting innovation through smart manufacturing. However, it has been found that there are constraints in the adoption of cloud by manufacturing companies due to concerns about security. Existing studies that have identified cloud security risks have been limited to presenting general cloud security risks or technical security risks rather than focusing on the manufacturing industry. Therefore, this study aimed to identify cloud security risks in the manufacturing industry based on the actual concerns in the field. For this, expert interviews and literature research were conducted to newly identify cloud security risks in the manufacturing industry, and the adequacy and urgency of the selected security risks were verified through surveys. Based on this study, if a cloud security management system for the manufacturing industry is designed in the future, it is expected that the adoption of cloud in the manufacturing industry will be more activated.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.4
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• pp.1147-1161
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• 2023

The Internet of Things (IoT) requires a new processing model that will allow scalability in cloud computing while reducing time delay caused by data transmission within a network. Such a model can be achieved by using resources that are closer to the user, i.e., by relying on edge computing (EC). The amount of IoT data also grows with an increase in the number of IoT devices. However, building such a flexible model within a heterogeneous environment is difficult in terms of resources. Moreover, the increasing demand for IoT services necessitates shortening time delay and response time by achieving effective load balancing. IoT devices are expected to generate huge amounts of data within a short amount of time. They will be dynamically deployed, and IoT services will be provided to EC devices or cloud servers to minimize resource costs while meeting the latency and quality of service (QoS) constraints of IoT applications when IoT devices are at the endpoint. EC is an emerging solution to the data processing problem in IoT. In this study, we improve the load balancing process and distribute resources fairly to tasks, which, in turn, will improve QoS in cloud and reduce processing time, and consequently, response time.

• Journal of Internet Computing and Services
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• v.13 no.1
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• pp.65-74
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• 2012

Cloud services are used for improving business. Moreover, customer relationship management(CRM) approaches use social networking as tools to enhance services to customers. However, most cloud systems do not support the semantic structures, and because of this, vital information from social network sites is still hard to process and use for business strategy. This paper proposes a collaboration framework based on social semantic web for cloud system. The proposed framework consists of components to support social semantic web to provide an efficient collaboration system for cloud consumers and service providers. The knowledge acquisition module extracts rules from data gathered by social agents and these rules are used for collaboration and business strategy. This paper showed the implementations of processing of social network site data in the proposed semantic model and pattern extraction which was used for the virtual grouping of cloud service providers for efficient collaboration.

• Journal of Internet Computing and Services
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• v.12 no.3
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• pp.37-47
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• 2011

Current researches in the Cloud focus on the appropriate interactions of cloud components in a large-scale system implementation. However, the current designs do not include intelligent methods like grouping the similar service providers based on their properties and integrating adaptive schemes for load distribution which can promote effective sharing of resource. This paper proposes an efficient virtualization of services by grouping the cloud providers to improve the service provisioning. The grouping of cloud service providers based on a cluster analysis collects the similar and related services in one group. The adaptive load balancing supports the service provisioning of the cloud system where it manages the load distribution within the group using an adaptive scheme. The proposed virtualization mechanism (GRALB) showed good results in minimizing message overhead and throughput performance compared to other methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.8
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• pp.3516-3541
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• 2018

Task scheduling is one of the most challenging aspects of cloud computing nowadays, and it plays an important role in improving overall performance in, and services from, the cloud, such as response time, cost, makespan, and throughput. A recent cloud task-scheduling algorithm based on the symbiotic organisms search (SOS) algorithm not only has fewer specific parameters, but also incurs time complexity. SOS is a newly developed metaheuristic optimization technique for solving numerical optimization problems. In this paper, the basic SOS algorithm is reduced, and chaotic local search (CLS) is integrated into the reduced SOS to improve the convergence rate. Simulated annealing (SA) is also added to help the SOS algorithm avoid being trapped in a local minimum. The performance of the proposed SA-CLS-SOS algorithm is evaluated by extensive simulation using the Matlab framework, and is compared with SOS, SA-SOS, and CLS-SOS algorithms. Simulation results show that the improved hybrid SOS performs better than SOS, SA-SOS, and CLS-SOS in terms of convergence speed and makespan.

• KIISE Transactions on Computing Practices
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• v.23 no.2
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• pp.85-96
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• 2017

IoT-Cloud service, integration of Internet of Things (IoT) and Cloud, is becoming a critical model for realizing creative and futuristic application services. Since IoT machines have little computing capacity, it is effective to attaching public Cloud resources for realizing IoT-Cloud service. Furthermore, utilizing containers and adopting a microservice architecture for developing IoT-Cloud service are useful for effective realization. The quality of microservice based IoT-Cloud service is affected by service function chaining which inter-connects each functions. For example, an issue with some of the functions or a bottleneck of inter-connection can degrade the service quality. To ensure functionality of the entire service, various test procedures considering various service environments are required to improve the service continuously. Hence in this paper, we introduce experimental realization of continuous integration based on DevOps and employ application performance monitoring for Node.js based IoT-Cloud service. Then we discuss its effectiveness.