• KIPS Transactions on Computer and Communication Systems
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• v.2 no.12
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• pp.553-560
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• 2013

The development of cloud computing technology and smart devices have increasingly been expanding the influence in various fields. Although DRM(Digital Rights Management) is a very important technology for secure content services, interoperability among DRM technologies must be addressed in order to provide the service without the constraints of time and place on various smart devices. In this paper, we study DRM Cloud which provides DRM functions as a service in cloud computing environment, and address interoperability problem by providing different DRM technologies as a cloud service. That is, when a user wants to play contents with the different DRM technologies on a smart device, the usage of the content is controlled by providing the corresponding DRM module and function as SaaS from DRM cloud. To do this, we define the functions and structure of DRM Proxy which performs smooth service call and provision between DRM cloud user and DRM cloud, and finally we describe the experimental implementation result.

• Journal of Information Processing Systems
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• v.9 no.2
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• pp.287-300
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• 2013

Cloud storage is provided as a service in order to keep pace with the increasing use of digital information. It can be used to store data via networks and various devices and is easy to access. Unlike existing removable storage, many users can use cloud storage because it has no storage capacity limit and does not require a storage medium. Cloud storage reliability has become a topic of importance, as many users employ it for saving great volumes of data. For protection against unethical administrators and attackers, a variety of cryptography systems, such as searchable encryption and proxy re-encryption, are being applied to cloud storage systems. However, the existing searchable encryption technology is inconvenient to use in a cloud storage environment where users upload their data. This is because this data is shared with others, as necessary, and the users with whom the data is shared change frequently. In this paper, we propose a searchable re-encryption scheme in which a user can safely share data with others by generating a searchable encryption index and then re-encrypt it.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.2
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• pp.826-845
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• 2017

Outsourcing computation is one of the most important applications in cloud computing, and it has a huge ability to satisfy the demand of data centers. Modular exponentiation computation, broadly used in the cryptographic protocols, has been recognized as one of the most time-consuming calculation operations in cryptosystems. Previously, modular exponentiations can be securely outsourced by using two untrusted cloud servers. In this paper, we present two practical and secure outsourcing modular exponentiations schemes that support only one untrusted cloud server. Explicitly, we make the base and the index blind by putting them into a matrix before send to the cloud server. Our schemes provide better performance in higher efficiency and flexible checkability which support single cloud server. Additionally, there exists another advantage of our schemes that the schemes are proved to be secure and effective without any cryptographic assumptions.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.6
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• pp.2824-2837
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• 2019

Container technologies are widely used in infrastructures to deploy and manage applications in cloud computing environment. As containers are light-weight software, the cluster of cloud applications can easily scale up or down to provide Internet-based services. Container-based applications can well deal with fluctuate workloads by dynamically adjusting physical resources. Current works of scheduling applications often construct applications' performance models with collected historical training data, but these works with static models cannot self-adjust physical resources to meet the dynamic requirements of cloud computing. Thus, we propose a self-adaptive automatic container scheduling framework AutoScale for cloud applications, which uses a feedback-based approach to adjust physical resources by extending, contracting and migrating containers. First, a queue-based performance model for cloud applications is proposed to correlate performance and workloads. Second, a fuzzy Kalman filter is used to adjust the performance model's parameters to accurately predict applications' response time. Third, extension, contraction and migration strategies based on predicted response time are designed to schedule containers at runtime. Furthermore, we have implemented a framework AutoScale with container scheduling strategies. By comparing with current approaches in an experiment environment deployed with typical applications, we observe that AutoScale has advantages in predicting response time, and scheduling containers to guarantee that response time keeps stable in fluctuant workloads.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.11
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• pp.4011-4027
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• 2021

Cloud Computing has emerged as an extensively used technology not only in the IT sector but almost in all sectors. As the nature of the cloud is distributed and dynamic, the jeopardies present in the current implementations of virtualization, numerous security threats and attacks have been reported. Considering the potent architecture and the system complexity, it is indispensable to adopt fundamentals. This paper proposes a secure authentication and data sharing scheme for providing security to the cloud data. An efficient IPSO-KELM is proposed for detecting the malicious behaviour of the user. Initially, the proposed method starts with the authentication phase of the data sender. After authentication, the sender sends the data to the cloud, and the IPSO-KELM identifies if the received data from the sender is an attacked one or normal data i.e. the algorithm identifies if the data is received from a malicious sender or authenticated sender. If the data received from the sender is identified to be normal data, then the data is securely shared with the data receiver using SHA256-RSA algorithm. The upshot of the proposed method are scrutinized by identifying the dissimilarities with the other existing techniques to confirm that the proposed IPSO-KELM and SHA256-RSA works well for malicious user detection and secure data sharing in the cloud.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.34 no.2
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• pp.207-215
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• 2016

The demand for spatial data service technologies is increasing lately with the development of realistic 3D spatial information services and ICT (Information and Communication Technology). Research is being conducted on the real-time provision of spatial data services through a variety of mobile and Web-based contents. Big data or cloud computing can be presented as alternatives to the construction of spatial data for the effective use of large volumes of data. In this paper, the process of building HLM (Hyper Live Map) using multi-source data to acquire stereo CCTV and other various data is presented and a big data service architecture design is proposed for the use of flexible and scalable cloud computing to handle big data created by users through such media as social network services and black boxes. The provision of spatial data services in real time using big data and cloud computing will enable us to implement navigation systems, vehicle augmented reality, real-time 3D spatial information, and single picture based positioning above the single GPS level using low-cost image-based position recognition technology in the future. Furthermore, Big Data and Cloud Computing are also used for data collection and provision in U-City and Smart-City environment as well, and the big data service architecture will provide users with information in real time.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.4
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• pp.889-903
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• 2018

Cloud computing has been activated globally due to the demands of the 4th industrial revolution and the efficient use of IT resources, and domestic usage is also increasing due to legislation and related laws. However, domestic financial companies are subject to various regulations due to the importance of their information and the ripple effects of accidents such as outflows. Only non-critical information processing systems that handle non-critical information are allowed to use cloud computing. Financial companies are required to set specific criteria and judgment to distinguish them. In this paper, we propose a method to enable the financial company cloud computing to be more active by specifying the ambiguous non - essential information processing system designation standard and making it easier to designate.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.12
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• pp.4682-4705
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• 2020

The Unmanned Aerial Vehicles (UAV) networks consisting of low-cost UAVs are very vulnerable to smart jammers that can choose their jamming policies based on the ongoing communication policies accordingly. In this article, we propose a novel cloud and edge-aided mobile communication scheme for low-cost UAV network against smart jamming. The challenge of this problem is to design a communication scheme that not only meets the requirements of defending against smart jamming attack, but also can be deployed on low-cost UAV platforms. In addition, related studies neglect the problem of decision-making algorithm failure caused by intermittent ground-to-air communication. In this scheme, we use the policy network deployed on the cloud and edge servers to generate an emergency policy tables, and regularly update the generated policy table to the UAVs to solve the decision-making problem when communications are interrupted. In the operation of this communication scheme, UAVs need to offload massive computing tasks to the cloud or the edge servers. In order to prevent these computing tasks from being offloaded to a single computing resource, we deployed a lightweight game algorithm to ensure that the three types of computing resources, namely local, edge and cloud, can maximize their effectiveness. The simulation results show that our communication scheme has only a small decrease in the SINR of UAVs network in the case of momentary communication interruption, and the SINR performance of our algorithm is higher than that of the original Q-learning algorithm.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.11
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• pp.2167-2175
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• 2017

In recent years, an IoT is dramatically developing according to the enhancement of AI, the increase of connected devices, and the high-performance cloud systems. Huge data produced by many devices and sensors is expanding the scope of services, such as an intelligent diagnostics, a recommendation service, as well as a smart monitoring service. The studies of edge computing are limited as a role of small server system with high quality HW resources. However, there are specialized requirements in a smart factory domain needed edge computing. The edges are needed to pre-process containing tiny filtering, pre-formatting, as well as merging of group contexts and manage the regional rules. So, in this paper, we extract the features and requirements in a scope of efficiency and robustness. Our edge offers to decrease a network resource consumption and update rules and learning models. Moreover, we propose architecture of edge computing based on learning factors sharing with a cloud system in a smart factory.

• KIPS Transactions on Computer and Communication Systems
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• v.7 no.2
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• pp.27-32
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• 2018

One of the crucial issues in mobile edge cloud computing environments is to monitor mobile devices. Due to the inherit properties of mobile devices, they are prone to unstable behavior that leads to failures. In order to satisfy the service level agreement (SLA), the mobile edge cloud administrators should take appropriate measures through a monitoring scheme. In this paper, we propose a monitoring scheme of mobile devices based on artificial intelligence in mobile edge cloud computing environments. The proposed monitoring scheme is able to measure faults of mobile devices based on previous and current monitoring information. To this end, we adapt the hidden markov chain model, one of the artificial intelligence technologies, to monitor mobile devices. We validate our monitoring scheme based on the hidden markov chain model. The proposed monitoring scheme can also be used in general cloud computing environments to monitor virtual machines.