• Proceedings of the Korean Society of Computer Information Conference
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• 2020.07a
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• pp.293-294
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• 2020

본 논문에서는 현재 이슈가 되고 있는 개인 정보 보안에 대해서 Keras 라이브러리를 사용하여 개인 정보 관련 데이터를 학습한 후, 한글 인식률 증가된 Tesseract-OCR 활용하여 사람들이 가지고 있는 데이터의 개인 정보 유무를 판단하여 분류한다.

• International Journal of Computer Science & Network Security
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• v.22 no.1
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• pp.61-68
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• 2022

The present study was based on developing an AI based model to facilitate the academic registration needs of blind students. The model was developed to enable blind students to submit academic service requests and tasks with ease. The findings from previous studies formed the basis of the study where functionality gaps from the literary research identified by blind students were utilized when the system was devised. Primary simulation data were composed based on several thousand cases. As such, the current study develops a model based on archival insight. Given that the model is theoretical, it was partially applied to help determine how efficient the associated AI tools are and determine how effective they are in real-world settings by incorporating them into the portal that institutions currently use. In this paper, we argue that voice-activated personal assistant (VAPA), text mining, bag of words, and case-based reasoning (CBR) perform better together, compared with other classifiers for analyzing and classifying the text in academic request submission through the VAPA.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.26 no.2
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• pp.463-474
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• 2016

As IT environment has changed, paths of information security in financial environment which is based on IT have become more diverse and damage caused by information leakage has been more serious. Among security incidents, personal information leakage incident is liable to give the greatest damage. Personal information leakage incident is more serious than any other types of information leakage incidents in that it may lead to secondary damage. The purpose of this study is to find how much personal information leakage incident influences corporate value by analyzing 21 cases of personal information leakage incident for the last 15 years 1,899 listing firm through case research method and inferring investors' response of to personal information leakage incident surveying a change in transaction before and after personal information leakage incident. This study made a quantitative analysis of what influence personal information leakage incident has on outcome of investment by types of investors by classifying types of investors into foreign investors, private investors and institutional investors. This study is significant in that it helps improve awareness of importance of personal information security by providing data that personal information leakage incident can have a significant influence on outcome of investment as well as corporate value in Korea stock market.

• Journal of Information Technology Services
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• v.14 no.3
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• pp.197-215
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• 2015

Recently, many internet users are using cloud computing. Users can manage, store and share their data and information by using personal cloud storage. In this paper, we aim to figure out influencing factors on personal cloud storage selection. The causal relationship between factors were identified through a importance analysis by using AHP(Analytic Hierarchy Process). AHP is a structured technique for organizing and analyzing complex decisions, based on mathematics and psychology. Research model consists of upper factorsincluding system factor, service factor and user factor. 12 lower factors and 6 alternatives were also analyzed. Asa result, system factor of 3 upper factors was found as the most important factor. Purpose-coincidence, security andaccessibility were top 3 factors among lower factors. N drive showed top importance value. We also conducted ANOVAby classifying 4 groups according to gender, age, currently used cloud and cloud to use. The results of this researchcan be useful guidelines for cloud computing industry.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.3
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• pp.1689-1701
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• 2019

In recent years, personal videos have been shared online due to the popular uses of portable devices, such as smartphones and action cameras. A recent report predicted that 80% of the Internet traffic will be video content by the year 2021. Several studies have been conducted on the detection of main video events to manage a large scale of videos. These studies show fairly good performance in certain genres. However, the methods used in previous studies have difficulty in detecting events of personal video. This is because the characteristics and genres of personal videos vary widely. In a research, we found that adding a dataset with the right perspective in the study improved performance. It has also been shown that performance improves depending on how you extract keyframes from the video. we selected frame segments that can represent video considering the characteristics of this personal video. In each frame segment, object, location, food and audio features were extracted, and representative vectors were generated through a CNN-based recurrent model and a fusion module. The proposed method showed mAP 78.4% performance through experiments using LSVC data.

• Journal of Convergence for Information Technology
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• v.11 no.12
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• pp.1-13
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• 2021

This study tried to suggest ways to improve the indicator system to strengthen the personal information protection. For this purpose, the components of indicator system are derived through domestic and foreign literature, and it was selected as main the diagnostic indicators through FGI/Delphi analysis for personal information protection experts and a survey for personal information protection officers of public institutions. As like this, this study was intended to derive an inspection standard that can be reflected as a separate index system for personal information protection, by classifying the specific IT technologies of the 4th industrial revolution, such as big data, cloud, Internet of Things, and artificial intelligence. As a result, from the planning and design stage of specific technologies, the check items for applying the PbD principle, pseudonymous information processing and de-identification measures were selected as 2 common indicators. And the checklists were consisted 2 items related Big data, 5 items related Cloud service, 5 items related IoT, and 4 items related AI. Accordingly, this study expects to be an institutional device to respond to new technological changes for the continuous development of the personal information management level diagnosis system in the future.

• Journal of Physiology & Pathology in Korean Medicine
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• v.30 no.6
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• pp.458-465
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• 2016

The information relating to the health of person has been increasing. The information is such as medical information and personal health record and the information collected by utilization and dissemination of mobile devices. Therefore, the interest and demand for systems that can integrate and manage the Personal Health Record(PHR) is increasing. Quantity and quality of information that is collected from the patient can have a major impact on the diagnosis and treatment of Korean Medicine(KM) in clinical practice. Because closely observe the usual clinical symptoms of patients to utilize the treatment. But if the interview when memories are not sure of the correct answer does not get much easier to find exactly the symptoms. So when recording original symptom(素證) and daily subjective symptom can be helpful for care. Therefore, the personal health care services that can record and manage and own is necessary based on KM. In this paper, we propose Korean Medicine Personal Health Record Platform(KM PHR Platform). We have selected the significant symptoms that mean to the personal records from symptom information required for diagnosis in KM. And classifying and scoring as the symptoms were used as personal health care indicators. And significant symptoms were easily configure a screen that can be recorded. simple operation is recorded as a symptom. It was designed to reflect these functions. So KM PHR Platform helps to Personal health care. Doctor may be able to help in the diagnosis and prognosis observation by reference to shared symptom. We look forward to a variety of health services based on KM using a symptom, a medical record, personal health device information.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.12
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• pp.873-879
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• 2020

The importance of data as a core resource of the 4th industrial revolution is emerging, and companies illegally collect and use personal data. In the financial sector, active research is conducted to safely manage personal data and provide better services using blockchain, big data, and AI technology. In this paper, we propose a system that can safely manage personal data by using blockchain technology, which can be used without changing the existing system. The composition of this system consists of a blockchain, blockchain linkages, a service provider, and a user (i.e., an app). Blockchain can be used regardless of its type and form, and services are provided by classifying blockchains and services in the blockchain linkages. Service providers can access personal data only after requesting and receiving delegated permission from users. Existent MyData services store all data in a user's mobile phone, so information may get leaked due to jailbreaks or rooting. But in the proposed system, personal data are stored in blockchain so information leakage can be prevented. In the future, we will study ways to provide customized services using personal data stored in blockchain.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.10
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• pp.3627-3641
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• 2021

Fintech, which stands for financial technology, is growing fast globally since the economic crisis hit the United States in 2008. Fintech companies are striving to secure a competitive advantage over existing financial services by providing efficient financial services utilizing the latest technologies. Fintech companies can be classified into several areas according to their business solutions. Among the Fintech sector, peer-to-peer (P2P) lending companies are leading the domestic Fintech industry. P2P lending is a method of lending funds directly to individuals or businesses without an official financial institution participating as an intermediary in the transaction. The rapid growth of P2P lending companies has now reached a level that threatens secondary financial markets. However, as the growth rate increases, so does the potential risk factor. In addition to government laws to protect and regulate P2P lending, further measures to reduce the risk of P2P lending accidents have yet to keep up with the pace of market growth. Since most P2P lenders do not implement their own credit rating system, they rely on personal credit scores provided by credit rating agencies such as the NICE credit information service in Korea. However, it is hard for P2P lending companies to figure out the intentional loan default of the borrower since most borrowers' credit scores are not excellent. This study analyzed the voices of telephone conversation between the loan consultant and the borrower in order to verify if it is applicable to determine the personal credit score. Experimental results show that the change in pitch frequency and change in voice pitch frequency can be reliably identified, and this difference can be used to predict the loan defaults or use it to determine the underlying default risk. It has also been shown that parameters extracted from sample voice data can be used as a determinant for classifying the level of personal credit ratings.

• ETRI Journal
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• v.33 no.6
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• pp.871-879
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• 2011

Because a wide variety of multimedia services are provided through personal wireless communication devices, the demand for efficient bandwidth utilization becomes stronger. This demand naturally results in the introduction of the variable bitrate speech coding concept. One exemplary work is the selectable mode vocoder (SMV) that supports speech/music classification. However, because it has severe limitations in its classification performance, a couple of works to improve speech/music classification by introducing support vector machines (SVMs) have been proposed. While these approaches significantly improved classification accuracy, they did not consider correlations commonly found in speech and music frames. In this paper, we propose a novel and orthogonal approach to improve the speech/music classification of SMV codec by adaptively tuning SVMs based on interframe correlations. According to the experimental results, the proposed algorithm yields improved results in classifying speech and music within the SMV framework.