Kiseok Choi

A privacy data leakage prevention method in P2P networks

Registered peers to a P2P service can share and exchange information with other peers without servers using P2P network. In such P2P networks, there are frequent leaks of the internal privacy data of an organization through P2P file sharing. Today, DLP which is a privacy data leakage prevention technology is applied P2P network blocking and file encryption methods. However restricting all the services and normal users is difficult due to the number of ports used by P2P including the port 80. Thus, we propose a privacy data leakage prevention method by releasing a P2P sharing file that does not include privacy data using a privacy data removing technology with a privacy data leaking risk factor. The proposed method provides higher security and performance compared with a DLP method as privacy data is removed from a P2P sharing file.

Information Interoperability System Using Multi-agent with Security

Recently, due to the interoperation of distributed information, the task and task analysis requiring the information connection in the distributed environment increases, and the decision support using the system becomes more important. As the centralized structure of the multi-agent based information interoperability system is composed of master and slave, it is vulnerable to impersonation attack, integrity, non-repudiation, and privacy security. Therefore, this paper adopted EMAF for information interoperability to propose an information interoperability system with security. In addition, to overcome the security vulnerability that may happen to the proposed system based on multi-agent, we are going to propose an agent mutual authentication method where certificates and session keys are combined together. When the proposed method is applied to the information interoperation between the national R&D report registration management system and the project management organization research report management system, not only the information interoperations in the distributed environment but also the safe information interoperability are supplied.

Tag Based Collaborative Knowledge Management System with Crowdsourcing

Many researchers have taken an interest in a collective intelligence the development of the user-generated web. Knowledge publishing and management with interactions between users are mostly a social revolution from the wisdom of crowds. This paper proposes a tag based collaborative knowledge management system. It can be realized both the collective intelligence with the crowdsourcing system and the dynamic categorization with the collaborative tagging approach. In addition, we establish the trustworthy knowledge through significant role allotment between a user group and a knowledge expert group.

Data Quality Management Based on Data Profiling in E-Government Environments

Recently, it is important to manage the quality of e-government data for guaranteeing the reliability of e-government to the public and being consistent in interoperating that data among legacy information systems in e-government environments. Data quality management is to try removing an invalid data and manipulating an incomplete or a revisable data on running database. So it is necessary that discovering, analyzing and reviewing that data which caused distrust in e-government. Data profiling is a process of analyzing location, structure, value of data and data profile included metadata for managing the valid data among that systems. In this paper, we present an approach of data profiling among legacy information systems in e-government environments to manage the quality of data and to guarantee the reliability of e-government to the public. So we enumerate some invalid, incomplete and revisable data that is needed to manipulate in proper. The proposed approach will be caused the advanced data quality of e-government and reliability to the public.

Development of Platform-Based Knowledge Map Service to get Data Insights of R&D Institution on User-Interested Subjects

Generally, any database is developed for the purpose of providing certain services for retrieval or management of data. If rules or processes designed to extract and analyze data in consideration of data characteristics are simplified and services are provided to users by visualizing analysis results in an easy-to-understand fashion, users will be able to get diverse insights easily without much cost or effort. If this kind of database is constructed and provided in NTIS database as well (e.g., project data, research outcome data such as paper and patent, personnel information, etc.) users might be able to get varied insights on national R&D data. The approach proposed in this study is significant in that it provides platform-based analysis services which can create a knowledge map after extracting and analyzing user-interested data and subsequently visualizing the results. Furthermore, this study is intended to make users obtain insight on a research institute more easily by providing analyzable additional information, not only as a list of research institutes in user-preferred subjects after providing the results of the analysis on R&D expenses, but also in regional distribution and annual changes in core research institutes, focusing on those taking part in national R&D programs in certain fields.

Study of Methods for Element Data Service for Electronic Documents Related to National R&D Projects

Abstract Major countries are supporting new knowledge creation and innovative activities by opening data so that the research results carried out by government budgets can serve as public goods. The Republic of Korea has also made research and development (R&D) reports and papers available in electronic file format, which is the result of national R&D programs for the general public. However, the extraction of meaningful information among unstructured data in text format has not satisfied researchers’ expectations. In order to evolve into a customized service reflecting the opinions of researchers, we investigated the demand for necessary contents and services at the planning stage of R&D projects. This study attempts to propose a method to offer significant information which shows a bigger unit than objects based on trend information with extraction and processing demands focusing on R&D reports based on the results of questionnaire survey and interviews with researchers. This study aims to provide the integration service, tentatively named ‘element data service’, of key sentences and table/figure images with a high demand for the utilization of researchers. The main procedure of the proposed method consists of the subject classification of the R&D report, the extraction of table/figure image, and the extraction of main sentence. We used public reports of the same classification published from 2012 to 2016 for the experiment and utilized the extractive summarization method for the copyright protection of report original text. After realizing the simple prototype, we examined the service possibility through the researcher target reviews.

CAPNet: An Enhanced Load Balancing Clustering Algorithm for Prolonging Network Lifetime in WSNs

A wireless sensor network consisting of resources, size, and cost-limited sensors is used in many military and civil applications. This paper proposes an energy-efficient clustering algorithm that extends the lifetime of sensor networks. The proposed clustering algorithm is an extended hierarchical clustering protocol that minimizes the overall amount of consumed energy in the network. The proposed approach dynamically updates clusters and distributes the load on the heavily loaded cluster heads among different nodes. It also balances the remaining energy on nodes in the network, which leads to prolonged network lifetime. The performance is evaluated in terms of network lifetime, average energy consumption, and standard deviation of residual energy.

A Design of R&D Planning Workbench Based on Research Outcome Analysis

Open collaboration based R&D activities such as research planning, development, and sharing of outcome has become an important issue recently. However, a information analysis system for supporting open collaboration based R&D planning is not sufficient. For this reason, R&D planning is dependent on the knowledge of a few planning experts, rather than the result of information analysis. This paper proposes a service model for supporting research planning based on outcome analysis in the phase of R&D planning, called R&D planning workbench. The R&D planning workbench is composed of R&D planning activities, R&D planning support services, and three different types of outcome analysis tools. We expect to enhance competitive power by constructing global collaborative network, promoting strategies by objective evaluation respect to research performance of individuals or institutions.

Dynamic Relation Management of Hierarchical Data for Interoperability in Distributed Environments

Data heterogeneity and complexity of its relation make difficult to interoperate among legacy information system in a geographically distributed environment. It is necessary to find a way to guarantee the reliability of interoperation among them. In reality, it is difficult to deal with this kind of problems only through the relational mapping because its nature of volatility, the relationship of hierarchical data tends to be frequently changed and modified by the policy. Therefore, this paper describes the several cases on changed relation in distributed and hierarchical data in context of the interconnection with legacy information systems on going, and presents the solution based on hybrid-XMDR which was proposed by our pre-researches. Hybrid-XMDR is a conceptual approach as the solution dealing with data and relation heterogeneity in the distributed information systems. It is extended to handle the relationship of hierarchical data from our pre-researches. In pre-researches, we have applied XMDR for interoperability to solve the data heterogeneity in the distributed environments and overcome the heterogeneity of structure, semantic and representation. In this paper, we attempt to apply for solving the two problems such as data and relation heterogeneity using hybrid-XMDR. It is expected to reduce the heterogeneity problems for interoperating among the distributed and independent legacy systems.

Big data and rule-based recommendation system in Internet of Things

This paper proposes a recommendation system based on big data framework and rule-based system in the era of Internet of Things. With the emergence of the smart devices beginning from smart phones extends to the general electronic devices such as smart tv sets, refrigerators, washing machines, robot vacuums, and so on. Such smart devices make it possible to collect the device-usage logs of end users whereby a system is able to analyze it to find the usage patterns of the end users and make recommendations to the end users. Furthermore, this allows to make recommendations on the individual users since the smart devices have their own identifiers such as MAC address and IPv6 address. The smart devices also have matched information with the end user id/s. In this study, we propose a method for analyzing the devise-usage patterns in semi-real time based on the big-data system architecture. We also present a recommendation framework which makes device-usage recommendations by using a rule-based system architecture with the analyzed usage patterns. Lastly, we introduce a segmentation-based analysis and recommendation framework to make recommendations based not only on his or her own usage patterns, but also on the common usage patterns of the users who are living in a similar context. The segmentation is formed also based on the types of the device usages, so that the analysis can be performed in a batch process thereby enabling to make the recommendations in real time based on the pre-analyzed usage patterns.