• Proceedings of the Korea Information Processing Society Conference
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• 2013.05a
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• pp.478-479
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• 2013

Smart home is one of the emerging domains to come up after advances in home appliances and automation technologies. There are many commercial solutions for smart homes yet many of them have yet to truly exploit the potential of private cloud for low level contextual services and ability to migrate to public cloud for more processing and storage. We propose a private cloud gateway for smart home which will have the ability to sense the new devices, ability to detect context of the situation and act in an appropriate way. It will also record the user logs which will be audited for improvement of the overall system.

• Journal of Information Processing Systems
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• v.8 no.4
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• pp.555-566
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• 2012

The cloud environment makes it possible to analyze large data sets in a scalable computing infrastructure. In the bioinformatics field, the applications are composed of the complex workflow tasks, which require huge data storage as well as a computing-intensive parallel workload. Many approaches have been introduced in distributed solutions. However, they focus on static resource provisioning with a batch-processing scheme in a local computing farm and data storage. In the case of a large-scale workflow system, it is inevitable and valuable to outsource the entire or a part of their tasks to public clouds for reducing resource costs. The problems, however, occurred at the transfer time for huge dataset as well as there being an unbalanced completion time of different problem sizes. In this paper, we propose an adaptive resource-provisioning scheme that includes run-time data distribution and collection services for hiding the data transfer time. The proposed adaptive resource-provisioning scheme optimizes the allocation ratio of computing elements to the different datasets in order to minimize the total makespan under resource constraints. We conducted the experiments with a well-known sequence alignment algorithm and the results showed that the proposed scheme is efficient for the cloud environment.

• Journal of Korea Multimedia Society
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• v.25 no.10
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• pp.1433-1447
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• 2022

Recently, as the demand for physical security technology to prevent leakage of technical and business information of companies and public institutions increases, the high tech companies are operating X-ray security checkpoints at building entrances to protect their intellectual property and technology. X-ray security checkpoints are operated to detect cameras and storage media that may store or leak important technologies in the bags of people entering and leaving the building. In this study, we propose an X-ray security checkpoint system that automatically detects a storage medium in an X-ray image using a deep learning based object detection method. The proposed system consists of an edge computing unit and a cloud-computing unit. We employ the RetinaNet for automatic storage media detection in the X-ray security checkpoint images. The proposed approach achieved mAP of 95.92% on private dataset.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.1
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• pp.83-94
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• 2015

Deduplication, which is a technique of eliminating redundant data by storing only a single copy of each data, provides clients and a cloud server with efficiency for managing stored data. Since the data is saved in untrusted public cloud server, however, both invasion of data privacy and data loss can be occurred. Over recent years, although many studies have been proposed secure deduplication schemes, there still remains both the security problems causing serious damages and inefficiency. In this paper, we propose secure and efficient client-side deduplication with Key-server based on Bellare et. al's scheme and challenge-response method. Furthermore, we point out potential risks of client-side deduplication and show that our scheme is secure against various attacks and provides high efficiency for uploading big size of data.

• International Journal of Advanced Culture Technology
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• v.7 no.4
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• pp.208-221
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• 2019

The inference for this research was concentrated on client's data protection in cloud computing i.e. data storages protection problems and how to limit unauthenticated access to info by developing access control model then accessible preparations were introduce after that an access control model was recommend. Cloud computing might refer as technology base on internet, having share, adaptable authority that might be utilized as organization by clients. Compositely cloud computing is software's and hardware's are conveying by internet as a service. It is a remarkable technology get well known because of minimal efforts, adaptability and versatility according to client's necessity. Regardless its prevalence large administration, propositions are reluctant to proceed onward cloud computing because of protection problems, particularly client's info protection. Management have communicated worries overs info protection as their classified and delicate info should be put away by specialist management at any areas all around. Several access models were accessible, yet those models do not satisfy the protection obligations as per services producers and cloud is always under assaults of hackers and data integrity, accessibility and protection were traded off. This research presented a model keep in aspect the requirement of services producers that upgrading the info protection in items of integrity, accessibility and security. The developed model helped the reluctant clients to effectively choosing to move on cloud while considerate the uncertainty related with cloud computing.

• Journal of the Korean Association of Geographic Information Studies
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• v.16 no.4
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• pp.141-152
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• 2013

The applications and concerned technologies of cloud computing services, one of major trends in the information communication technology, are widely progressing and advancing. OpenStack, one of open source cloud computing platforms, is comprised of several service components; using these, it can be possible to build public or private cloud computing service for a given target application. In this study, a remote sensing image analysis processing service on cloud computing environment has designed and implemented as an operational test application in the private cloud computing environment based on OpenStack. The implemented service is divided into instance server, web service, and mobile app. A instance server provides remote sensing image processing and database functions, and the web service works for storage and management of remote sensing image from user sides. The mobile app provides functions for remote sensing images visualization and some requests.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.4
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• pp.347-357
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• 2019

The usefulness of processing and analysis systems of GIS-based agricultural climate data is affected by the reliability and availability of computing infrastructures such as cloud, on-premises, and hybrid. Cloud technology has grown in popularity. However, various reference cases accumulated over the years of operational experiences point out important features that make on-premises technology compatible with cloud technology. Both cloud and on-premises technologies have their advantages and disadvantages in terms of operational time and cost, reliability, and security depending on cases of applications. In this study, we have described characteristics of four general computing platforms including cloud, on-premises with hardware-level virtualization, on-premises with operating system-level virtualization and hybrid environments, and compared them in terms of advantages and disadvantages when a huge amount of GIS-based agricultural climate data were stored and processed to provide public services of agro-meteorological and climate information at high spatial and temporal resolutions. It was found that migrating high-resolution agricultural climate data to public cloud would not be reasonable due to high cost for storing a large amount data that may be of no use in the future. Therefore, we recommended hybrid systems that the on-premises and the cloud environments are combined for data storage and backup systems that incur a major cost, and data analysis, processing and presentation that need operational flexibility, respectively.

• Journal of KIISE
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• v.43 no.8
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• pp.933-945
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• 2016

Sharing data by multiple users on the public storage, e.g., the cloud, is considered to be efficient because the cloud provides on-demand computing service at anytime and anywhere. Secure data sharing is achieved by fine-grained access control. Existing symmetric and public key encryption schemes are not suitable for secure data sharing because they support 1-to-1 relationship between a ciphertext and a secret key. Attribute based encryption supports fine-grained access control, however it incurs linearly increasing ciphertexts as the number of attributes increases. Additionally, the decryption process has high computational cost so that it is not applicable in case of resource-constrained environments. In this study, we propose an efficient attribute-based secure data sharing scheme with outsourceable decryption. The proposed scheme guarantees constant-size ciphertexts irrespective of the number of attributes. In case of static attributes, the computation cost to the user is reduced by delegating approximately 95.3% of decryption operations to the more powerful storage systems, whereas 72.3% of decryption operations are outsourced in terms of dynamic attributes.

• Journal of Digital Convergence
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• v.14 no.2
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• pp.175-181
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• 2016

Cloud storage servers do not detect duplication of conventionally encrypted data. To solve this problem, convergent encryption has been proposed. Recently, various client-side deduplication technology has been proposed. However, this propositions still cannot solve the security problem. In this paper, we suggest a secure source-based deduplication technology, which encrypt data to ensure the confidentiality of sensitive data and apply proofs of ownership protocol to control access to the data, from curious cloud server and malicious user.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.05a
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• pp.472-474
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• 2017

Storage of files is important in the information age. In particular, files in the company should be managed more efficiently. In this paper, we connect to FTP Server through Client program to manage files efficiently. The FTP Server builds a database to manage employee information in the company and folder paths for each employee. The Client program can be used after login with the assigned employee number and password. You can check the list of files stored in the FTP server when login is successful and upload, download and delete after selecting specific files. Folders can also be created and deleted for more systematic management of files. For the efficient operation of the company, it is implemented so that it can be divided into a personal drive and a public drive.