• Journal of the Korean association of regional geographers
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• v.2 no.2
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• pp.1-8
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• 1996

The purpose of this study is to analyse the regional variation of tenancy system in later Yi-Dynasty in Korea. Materials for the analysis are acquired through materials(韓國土地農産調査報告), published in 1905 and agricultural census published in 1912. For the identification of difference of tenancy system between agricultural region, regionalization are conducted through by crop combination. Crop combination structure, using rank of LQ index, was clustered into five generic lesions through cluster analysis. In these contexts, this study has come to following conclusions. There are three types of tenancy system in materials; (1) Doji(賭地) system of which landrent was 1/3 agricultural products. Tenant healed the land tax and seeds. (2) Byoengjak(竝作) system of which landrent was 1/2 agricultural products. Landlords healed the land tax and seeds (3) Jeongaek(定額法) system of which landrent was fixed without relation to annual products. But through the analysis of relationship between agricultural region and tenancy system, a new tenancy system could be identified : Byeongjak(竝作) II system. In this system, landrent was 1/2 of agricultural products, but landlord and tenant shared the landtax and seed in common. In the distribution of these systems, relationship between tenancy system and agricultural regions could be identified. Doji system was distributed in the regions where rice and double cropping was specialized. But Byoeongjak(竝作) system was distributed in the regions where upland crops are specialized and ratio of Paddy field is comparatively low. Especially new types were emenged where ratio of paddy field was very low. These show that increase of productivity of land didn't induce the development of the right of ownership in land. The development of ownership was emerged only on the rice paddy fields. Barley cultivated through double-cropping passed into tenant's possessions. So nominal landrent in paddy field seemed to be raised, but actual landrent was maintained about 1/3 of Products through double cropping. On the contrary, rights of cultivation is developed through double cropping. As double cropping is developed, competition on paddy field between tenants was intensified. Consequently nominal land rent of Paddy fields should be raised.

• Convergence Security Journal
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• v.24 no.1
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• pp.35-42
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• 2024

Multi-tenancy architecture plays a crucial role in cloud-based services and applications, and data isolation within such environments has emerged as a significant security challenge. This paper investigates various data isolation methods including schema-based isolation, logical isolation, and physical isolation, and compares their respective advantages and disadvantages. It evaluates the practical application and effectiveness of these data isolation methods, proposing security considerations and selection criteria for data isolation in the development of multi-tenant websites. This paper offers important guidance for developers, architects, and system administrators aiming to enhance data security in multi-tenancy environments. It suggests a foundational framework for the design and implementation of efficient and secure multi-tenant websites. Additionally, it provides insights into how the choice of data isolation methods impacts system performance, scalability, maintenance ease, and overall security, exploring ways to improve the security and stability of multi-tenant systems.

• Journal of Multimedia Information System
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• v.7 no.2
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• pp.125-136
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• 2020

Nowadays, cloud computing is becoming more popular among companies. However, the characteristics of cloud computing such as a virtualized environment, constantly changing, possible to modify easily and multi-tenancy with a distributed nature, it is difficult to perform attack detection with traditional tools. This work proposes a solution which aims to collect traffic packets data by using Flume and filter them with Spark Streaming so it is possible to only consider suspicious data related to HTTP Slow Rate Denial-of-Service attacks and reduce the data that will be stored in Hadoop Distributed File System for analysis with the FP-Growth algorithm. With the proposed system, we also aim to address the difficulties in attack detection in cloud environment, facilitating the data collection, reducing detection time and enabling an almost real-time attack detection.

• Journal of Information Processing Systems
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• v.17 no.4
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• pp.675-689
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• 2021

Nowadays, cloud computing is being adopted for more organizations. However, since cloud computing has a virtualized, volatile, scalable and multi-tenancy distributed nature, it is challenging task to perform attack detection in the cloud following conventional processes. This work proposes a solution which aims to collect web server logs by using Flume and filter them through Spark Streaming in order to only consider suspicious data or data related to denial-of-service attacks and reduce the data that will be stored in Hadoop Distributed File System for posterior analysis with the frequent pattern (FP)-Growth algorithm. With the proposed system, we can address some of the difficulties in security for cloud environment, facilitating the data collection, reducing detection time and consequently enabling an almost real-time attack detection.

• Communications for Statistical Applications and Methods
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• v.25 no.5
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• pp.489-499
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• 2018

Jeonse is a unique property rental system in Korea in which a tenant pays a part of the price of a leased property as a fixed amount security deposit and gets back the entire deposit when the tenant moves out at the end of the tenancy. Jeonse deposit is very important in the Korean real estate market since it is directly related to the residential property sales price and it is a key indicator to predict future real estate market trend. Jeonse deposit data shows a skewed and heteroscedastic distribution and the commonly used mean regression model may be inappropriate for the analysis of Jeonse deposit data. In this paper, we apply a Bayesian quantile regression model to analyze Jeonse deposit data, which is non-parametric and does not require any distributional assumptions. Analysis results show that the quantile regression coefficients of most explanatory variables change dramatically for different quantiles. The regression coefficients of some variables have different signs for different quantiles, implying that even the same variable may affect the Jeonse deposit in the opposite direction depending on the amount of deposit.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.6
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• pp.1031-1041
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• 2020

Cloud computing technology plays an important role in the deep learning industry as deep learning services are deployed frequently on top of cloud infrastructures. In such cloud environment, virtualization technology provides logically independent and isolated computing space for each tenant. However, recent studies demonstrate that by leveraging vulnerabilities of virtualization techniques and shared processor architectures in the cloud system, various side-channels can be established between cloud tenants. In this paper, we propose a novel attack scenario that can steal internal information of deep learning models by exploiting the Meltdown vulnerability in a multi-tenant system environment. On the basis of our experiment, the proposed attack method could extract internal information of a TensorFlow deep-learning service with 92.875% accuracy and 1.325kB/s extraction speed.