• Journal of the Korea Institute of Information Security & Cryptology
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• v.23 no.5
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• pp.831-847
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• 2013

The various types of data are being created in large quantities resulting from the spread of social media and the mobile popularization. Many companies want to obtain valuable business information through the analysis of these large data. As a result, it is a trend to integrate the big data technologies into the company work. Especially, Hadoop is regarded as the most representative big data technology due to its terabytes of storage capacity, inexpensive construction cost, and fast data processing speed. However, the authentication token system of Hadoop Distributed File System(HDFS) for the user authentication is currently vulnerable to the replay attack and the datanode hacking attack. This can cause that the company secrets or the personal information of customers on HDFS are exposed. In this paper, we analyze the possible security threats to HDFS when tokens or datanodes are exposed to the attackers. Finally, we propose the secure authentication protocol in HDFS based on hash chain.

• The KIPS Transactions:PartC
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• v.19C no.1
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• pp.19-28
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• 2012

Wireless sensor network provides services anytime and anywhere they are requested. Especially, medical sensor network based on biosensors is applied a lot to biotechnology and medical engineering. In medical sensor network, people can make their health checked at home free from temporal and spatial constraints. In ubiquitous healthcare environment, people can get instant help even in the emergency, and in hospital, patients can be taken care of efficiently. In this environment, health and life related data are delivered, and the privacy and security of personal data are very important. In this paper, we propose user authentication and data communication mechanism in two modes, normal and urgent situation using cellular phone. Through our proposal, data can be transferred in quick and secure manner.

• Journal of the Korea Society of Computer and Information
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• v.25 no.6
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• pp.65-72
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• 2020

Today, the Internet of Things is used in many places, including homes, industrial sites, and hospitals, to give us convenience. Many services generate new value through real-time data collection, storage and analysis as devices are connected to the network. Many of these fields are creating services and applications that utilize sensors and communication functions within IoT devices. However, since everything can be hacked, it causes a huge privacy threat to users who provide data. For example, a variety of sensitive information, such as personal information, lifestyle patters and the existence of diseases, will be leaked if data generated by smarwatches are abused. Development of IoT must be accompanied by the development of security. Recently, Differential Privacy(DP) was adopted to privacy-preserving data processing. So we propose the method that can aggregate health data safely on smartwatch platform, based on DP.

• Journal of Information Technology and Architecture
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• v.11 no.1
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• pp.63-73
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• 2014

As the advance of WWW, unstructured data including texts are taking users' interests more and more. These unstructured data created by WWW users represent users' subjective opinions thus we can get very useful information such as users' personal tastes or perspectives from them if we analyze appropriately. In this paper, we provide various analysis efficiently for unstructured text documents by taking advantage of OLAP (On-Line Analytical Processing) multidimensional cube technology. OLAP cubes have been widely used for the multidimensional analysis for structured data such as simple alphabetic and numberic data but they didn't have used for unstructured data consisting of long texts. In order to provide multidimensional analysis for unstructured text data, however, Text Cube model has been proposed precently. It incorporates term frequency and inverted index as measurements to search and analyze text databases which play key roles in information retrieval. The primary goal of this paper is to apply this text cube model to a real data set from in an Internet site sharing hotel information and to provide multidimensional analysis for users' reviews on hotels written in texts. To achieve this goal, we first build text cubes for the hotel review data. By using the text cubes, we design and implement the system which provides multidimensional keyword search features to search and to analyze review texts on various dimensions. This system will be able to help users to get valuable guest-subjective summary information easily. Furthermore, this paper evaluats the proposed systems through various experiments and it reveals the effectiveness of the system.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.7
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• pp.277-282
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• 2015

HL7 released V3 CDA(Clinical Document Architecture) and V2.x message standards for medical information exchange. Currently, these standards are successfully adopted by a number of nations across the globe. However, substantial amount of time is required to develop and implement these standards. Moreover, developers need a lot of time to understand these standards. To solve these issues from 2011, the HL7 standard framework started to discuss Fast Healthcare Interoperability Resources(FHIR) as next generation standard of healthcare information exchange. People's interests toward personal health record and smartphone penetration rate are growing and increasing rapidly. Therefore, our research team believes it is necessary to develop a PHR profiling system which could be accessed by using a smartphone and we developed the system. Through a FHIR Profile editor tool developed in Furore, we found that improvements could be made in generating and changing the profile. In order to build the PHR Profiling system, an Open-API on FHIR is used for exchanging information between electronic medical record system and PHR Profiling system. In the PHR Profiling system, the transactions of information between two systems are provided by RESTful service. In this study, we verify the efficiency of development of the PHR Profiling system through FHIR.

• Journal of the Korean Society for information Management
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• v.25 no.3
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• pp.27-39
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• 2008

In bibliographic data, the use of personal names to indicate authors makes it difficult to specify a particular author since there are numerous authors whose personal names are the same. Resolving same-name author instances into different individuals is called author resolution, which consists of two steps: calculating author similarities and then clustering same-name author instances into different person groups. Author similarities are computed from similarities of author-related bibliographic features such as coauthors, titles of papers, publication information, using supervised or unsupervised methods. Supervised approaches employ machine learning techniques to automatically learn the author similarity function from author-resolved training samples. So far however, a few machine learning methods have been investigated for author resolution. This paper provides a comparative evaluation of a variety of recent high-performing machine learning techniques on author disambiguation, and compares several methods of processing author disambiguation features such as coauthors and titles of papers.

• Journal of the Korea Society of Computer and Information
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• v.19 no.5
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• pp.27-35
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• 2014

The characteristics of today's health care industry, based on the state of the art IT can be represented as a paradigm of human-oriented ubiquitous and accessible as possible by U-Health care. In addition, the healthcare industry is information and communication technologies (ICT) developments regarding the many advances and applications based on the research being carried out actively. Medical information system has been developed toward combining information systems of medical IT and it sets its sights on the fusion of developed IT and u-healthcare system. So changing distributed medical information systems into a safe PHR integrated system based on IaaS cloud computing is suggested in order to forge u-healthcare system with the times in this paper. Our experimental results show that our proposed system increased the data access time by about 24% and reduces the waiting time for processing service by about 4.3% over the web-based PHR.

• The KIPS Transactions:PartB
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• v.14B no.6
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• pp.401-406
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• 2007

This study discusses the development of a u-Health care system that can detect quickly and cope actively with emergent situations of chronic disease patients who lead everyday life. If a patient's emergent situation is detected by personal health care host(PHCH), which is composed of acceleration and vibration sensors, GPS, and CDMA communication module, a text message on the patient's current location is transmitted to the hospital and the guardian's mobile terminal so that they can cope with the situation immediately. Especially, the system analyzes data from sensors by using neural network and determines emergent situations such as syncope and convulsion promptly. The exact location of patients can also be found in the electronic map by using GPS information. The experiments show that this system is very effective to find emergencies promptly for chronic disease patients who cannot take care of themselves and it is expected to save many lives.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.6
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• pp.1323-1328
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• 2021

In this paper, we present a medical electric scooter sharing platform for the transportation vulnerable who are experiencing difficulties and inconveniences in moving. The proposed medical electric scooter sharing platform for the transportation vulnerable includes basic mobile rental, return, and functions that incorporate the IOT technology of the currently operating personal mobility sharing platform. The safety function has been strengthened. The medical electric scooter sharing platform for the transportation vulnerable stores driving data on the server in real time through GPS, and strengthens the alarm and call function in advance of an accident to enable rapid SOS processing. By making the quick contact and responding to the situation, people with disabilities can drive safely and comfortably.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.4
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• pp.11-19
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• 2023

The paper proposes a smart mirror system that recommends fragrances based on user emotion analysis. This paper combines natural language processing techniques such as embedding techniques (CounterVectorizer and TF-IDF) and machine learning classification models (DecisionTree, SVM, RandomForest, SGD Classifier) to build a model and compares the results. After the comparison, the paper constructs a personal emotion-based fragrance recommendation mirror model based on the SVM and word embedding pipeline-based emotion classifier model with the highest performance. The proposed system implements a personalized fragrance recommendation mirror based on emotion analysis, providing web services using the Flask web framework. This paper uses the Google Speech Cloud API to recognize users' voices and use speech-to-text (STT) to convert voice-transcribed text data. The proposed system provides users with information about weather, humidity, location, quotes, time, and schedule management.