• Journal of the Korea Institute of Information Security & Cryptology
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• v.23 no.6
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• pp.1267-1276
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• 2013

Cloud consumers who use cloud computing services are obliged to review and monitor the legal compliance of cloud providers who are consigned the processes of the PII (personally identifiable information) from them. This paper presented possible scenarios for cloud PII outsourcing and suggested PIMS (personal information management system) controls for outsourcing management between cloud consumers and cloud providers by analyzing both international standards and domestic certification schemes related to cloud computing and/or privacy management based on the legal obligations for PII outsourcing from Korean "Personal Information Protection Act (PIPA)". The controls suggested can be applicable for developing the guidance of complying with privacy laws in organizations or the checklist of PII outsourcing management in PIMS certification.

• International Journal of Advanced Culture Technology
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• v.5 no.2
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• pp.90-97
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• 2017

The Internet of Things(IoT) market is expected to grow from 15.5billion to 75.4 billion by 2015-2025. As the number of IoT devices increases, the amount of data that is sent to the cloud is increasing. Today's Cloud Computing models are not suited to handle the vast amount of data generated by IoT devices. In this paper, we propose a Cooperation Coordinator System that reduces server load and improved real-time processing capability under specific circumstances by using Band-Cloud. The cooperation coordinator system dynamically forms the cloud when cooperation is needed between mobile devices located near. It is called Band-Cloud. Band-Cloud provides services entrusted by Central Cloud. This paper describes the proposed system and shows the cooperation process using the Android-based mobile devices and Wi-Fi Direct technology. Such a system can be applied to cases where real-time processing is required in a narrow area such as a hospital ward or a school classroom.

• Journal of Digital Convergence
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• v.10 no.10
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• pp.465-473
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• 2012

The mobile devices and cloud computing technology introduced new content services such as N-Screen service. The DRM techniques have been developed to support interoperability and multi-platform for new environment of content service. Nevertheless, it is still inconvenient for the consumers to purchase a new DRM-supported device or to migrate some purchased contents into new device due to the change of the subscription of service. Therefore, in this paper, cloud-based DRM model which is referred as DRMaaS (DRM-as-a-Service) model, is proposed to allow the consumer to freely use and move some DRM-protected contents in various smart devices regardless of subscription of service.

• 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.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.3
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• pp.443-452
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• 2021

One of the key technologies in edge computing is that it always provides services close to the user by moving data between edge servers according to the user's movements. As such, the movement of data between edge servers is frequent. As IoT technology advances and usage areas expand, the data generated also increases, requiring technology to accurately track and process each data to properly manage the data present in the edge computing environment. Currently, cloud systems do not have data disposal technology based on tracking technology for data movement and distribution in their environment, so users cannot see where it is now, whether it is properly removed or not left in the cloud system if users request it to be deleted. In this paper, we propose a tracking data server to create and manage the movement and distribution of data for each edge server and data stored in the central cloud in an edge computing environment.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.12
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• pp.291-306
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• 2020

Nowadays, Data-Network-AI (DNA)-based intelligent services and applications have become a reality to provide a new dimension of services that improve the quality of life and productivity of businesses. Artificial intelligence (AI) can enhance the value of IoT data (data collected by IoT devices). The internet of things (IoT) promotes the learning and intelligence capability of AI. To extract insights from massive volume IoT data in real-time using deep learning, processing capability needs to happen in the IoT end devices where data is generated. However, deep learning requires a significant number of computational resources that may not be available at the IoT end devices. Such problems have been addressed by transporting bulks of data from the IoT end devices to the cloud datacenters for processing. But transferring IoT big data to the cloud incurs prohibitively high transmission delay and privacy issues which are a major concern. Edge computing, where distributed computing nodes are placed close to the IoT end devices, is a viable solution to meet the high computation and low-latency requirements and to preserve the privacy of users. This paper provides a comprehensive review of the current state of leveraging deep learning within edge computing to unleash the potential of IoT big data generated from IoT end devices. We believe that the revision will have a contribution to the development of DNA-based intelligent services and applications. It describes the different distributed training and inference architectures of deep learning models across multiple nodes of the edge computing platform. It also provides the different privacy-preserving approaches of deep learning on the edge computing environment and the various application domains where deep learning on the network edge can be useful. Finally, it discusses open issues and challenges leveraging deep learning within edge computing.

• The KIPS Transactions:PartC
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• v.19C no.1
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• pp.1-8
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• 2012

Recently, due to the development of broadband, there is a significant increase in utilizing on-demand Saas (Software as a Service) which takes advantage of the technology. Nevertheless, the academic and practical levels of digital forensics have not yet been established in cloud computing environment. In addition, the data of user behavior is not likely to be stored on the local system. The relevant data may be stored across the various remote servers. Therefore, the investigators may encounter some problems in performing digital forensics in cloud computing environment. it is important to analysis History files, Cookie files, Temporary Internet Files, physical memory, etc. in a viewpoint of client, since the SaaS basically uses the web to connects the internet service. In this paper, we propose the method that analysis the usuage trace of the Saas which is the one of the most popular cloud computing services.

• International Journal of Computer Science & Network Security
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• v.23 no.6
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• pp.17-26
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• 2023

Blockchain technology has emerged as one of the most crucial solutions in numerous industries, including healthcare. The combination of blockchain technology and cloud computing results in improving access to high-quality telemedicine and healthcare services. In addition to developments in healthcare, the operational strategy outlined in Vision 2030 is extremely essential to the improvement of the standard of healthcare in Saudi Arabia. The purpose of this survey is to give a thorough analysis of the current state of healthcare technologies that are based on blockchain and cloud computing. We highlight some of the unanswered research questions in this rapidly expanding area and provide some context for them. Furthermore, we demonstrate how blockchain technology can completely alter the medical field and keep health records private; how medical jobs can detect the most critical, dangerous errors with blockchain industries. As it contributes to develop concerns about data manipulation and allows for a new kind of secure data storage pattern to be implemented in healthcare especially in telemedicine fields is discussed diagrammatically.

• International journal of advanced smart convergence
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• v.8 no.2
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• pp.204-210
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• 2019

Mobile Crowd Computing is one of the most efficient and effective way to collect the Electronic health records and they are very intelligent in processing them. Mobile Crowd Computing can handle, analyze and process the huge volumes of Electronic Health Records (EHR) from the high-performance Cloud Environment. Electronic Health Records are very sensitive, so they need to be secured, authenticated and processed efficiently. However, security, privacy and authentication of Electronic health records(EHR) and Patient health records(PHR) in the Mobile Crowd Computing Environment have become a critical issue that restricts many healthcare services from using Crowd Computing services .Our proposed Efficient Multi-layer Encryption Framework(MLEF) applies a set of multiple security Algorithms to provide access control over integrity, confidentiality, privacy and authentication with cost efficient to the Electronic health records(HER)and Patient health records(PHR). Our system provides the efficient way to create an environment that is capable of capturing, storing, searching, sharing, analyzing and authenticating electronic healthcare records efficiently to provide right intervention to the right patient at the right time in the Mobile Crowd Computing Environment.

• Convergence Security Journal
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• v.15 no.7
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• pp.31-38
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• 2015

Recently the government civil affairs administration system has been advanced to a cloud computing environment from a simple network environment. The electronic civil affairs processing environment in recent years means cloud computing environment based bid data services. Therefore, there exist lots of problems in processing big data for the government civil affairs service compared to the conventional information acquisition environment. That is, it processes new information through collecting required information from different information systems much further than the information service in conventional network environments. According to such an environment, applications of providing administration information for processing the big data have been becoming a major target of illegal attackers. The objectives of this study are to prevent illegal uses of the electronic civil affairs service based on IPs nationally located in civil affairs centers and to protect leaks of the important data retained in these centers. For achieving it, the safety, usability, and security of services are to be ensured by using different authentication processes and encryption methods based on these processes.