• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.1
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• pp.187-198
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• 2018

Recently, financial transactions and electronic commerce in cyberspace are being performed more quickly and conveniently, with the development in diverse types of fintech and biometric authentication. But user authentication using passwords still occupies a big proportion even in these new services. therefore, safe creation and management of passwords is fundamental and indispensable to protect personal information and asset. This study examined the patterns of password usage by conducting a survey and analyzed factors influencing password security behavior intentions using the heath belief model. As a result, perceived susceptibility, perceived severity, perceived benefits, and perceived barriers significantly affected security behavior intentions, and especially, perceived severity had a moderating effect in other factors.

• Journal of Platform Technology
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• v.11 no.3
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• pp.3-11
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• 2023

Real-time control systems are growing rapidly as infrastructure technologies such as IoT and mobile communication develop and services that value real-time such as factory management and vehicle operation checks increase. Various solutions have been proposed to increase the time sensitivity of this system, but most real-time control systems are currently composed of local servers and multiple clients located in control stations, which are transmitted to local servers where control systems are located. In this paper, we proposed an edge computing-based real-time control model that can reduce the time it takes for the bus information system, one of the real-time control systems, to provide the information to the user at the time it collects the information. Simulating the existing model and the edge computing model, the edge computing model confirmed that the cost for users to receive data is reduced from at least 10% to up to 80% compared to the existing model.

• The Journal of Society for e-Business Studies
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• v.20 no.1
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• pp.117-135
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• 2015

Mobile wallet that can keep coupons and membership cards for mobile is one of rapidly growing services due to its usability and financial benefit. However, in spite of its rapid growth, the increase of users who do not use continuously it is an important consideration to service providers for making a profit. This study aims to test the effects of factors affecting the continuous use intention of mobile wallet based on VAM (Value-based Adoption Model) which can analyse them in both benefit and sacrifice aspects, so as to suggest considerations to increase the use period of mobile wallet for service providers. The research findings supported the hypotheses regarding to the effects of usefulness, value-expression, perceived security and enjoyment in the benefit aspect and technicality in the sacrifice aspect on perceived value. In addition, the causal path from perceived value to continuous use intention was significant. The study results are expected to be used in marketing or service improvement for short-term users by taking account of emotional factors as well as functional factors.

• Information Systems Review
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• v.25 no.4
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• pp.291-314
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• 2023

The concept of Mydata emerged with the expansion of the data economy. MyData aims to empower individuals by enhancing their right to self-determination over their personal data. The use of MyData is expected to enable the provision of innovative service in various fields. Since 2022, MyData has been introduced and actively used in the financial sector. In the future, not only financial institutions but also Bigtech and Fintech companies are expected to actively join and demonstrate rapid expansion. To ensure steady growth for MyData in the financial sector, it is necessary to assess acceptance behaviors from multiple perspectives. However, the majority of existing research solely focuses on positive acceptance. This study analyzed the impact of users' personal characteristics and innovation characteristics on both innovation resistance and acceptance resistance. The analysis revealed that personal and innovation characteristics contribute to an increase in distrust and innovation resistance in the MyData service. In addition, it has been confirmed that it can lead to actions such as delayed acceptance and refusal to accept. The results of this study offer both theoretical and practical insights into user behavior within the MyData service market.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.5
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• pp.477-481
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• 2008

Place recognition is necessary for a mobile user to be provided with place-dependent information. This paper proposes real-time video based place recognition system that identifies users' current place while moving in the building. As for the feature extraction of a scene, there have been existing methods based on global feature analysis that has drawback of sensitive-ness for the case of partial occlusion and noises. There have also been local feature based methods that usually attempted object recognition which seemed hard to be applied in real-time system because of high computational cost. On the other hand, researches using statistical methods such as HMM(hidden Markov models) or bayesian networks have been used to derive place recognition result from the feature data. The former is, however, not practical because it requires huge amounts of efforts to gather the training data while the latter usually depends on object recognition only. This paper proposes a combined approach of global and local feature analysis for feature extraction to complement both approaches' drawbacks. The proposed method is applied to a mobile information system and shows real-time performance with competitive recognition result.

• Journal of Internet Computing and Services
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• v.19 no.1
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• pp.37-47
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• 2018

The number of capabilities of Internet of Things (IoT) devices will exponentially grow over the next years. These devices may generate a vast amount of time-constrained data. In the context of IoT, data management should act as a layer between the objects and devices generating the data and the applications accessing the data for analysis purposes and services. In addition, most of IoT services will be content-centric rather than host centric to increase the data availability and the efficiency of data delivery. IoT will enable all the communication devices to be interconnected and make the data generated by or associated with devices or objects globally accessible. Also, fog computing keeps data and computation close to end users at the edge of network, and thus provides a new breed of applications and services to end users with low latency, high bandwidth, and geographically distributed. In this paper, we propose Edge-Fog cloud-based Hierarchical Data Delivery ($EFcHD^2$) method that effectively and reliably delivers IoT data to associated with IoT applications with ensuring time sensitivity. The proposed $EFcHD^2$ method stands on basis of fully decentralized hybrid of Edge and Fog compute cloud model, Edge-Fog cloud, and uses information-centric networking and bloom filters. In addition, it stores the replica of IoT data or the pre-processed feature data by edge node in the appropriate locations of Edge-Fog cloud considering the characteristic of IoT data: locality, size, time sensitivity and popularity. Then, the performance of $EFcHD^2$ method is evaluated through an analytical model, and is compared to fog server-based and Content-Centric Networking (CCN)-based data delivery methods.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.5
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• pp.1007-1017
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• 2019

Currently, most biometrics system authenticates users by using single biometric information. This method has many problems such as noise problem, sensitivity to data, spoofing, a limitation of recognition rate. One method to solve this problems is to use multi biometric information. The multi biometric authentication system performs information fusion for each biometric information to generate new information, and then uses the new information to authenticate the user. Among information fusion methods, a score-level fusion method is widely used. However, there is a problem that a normalization operation is required, and even if data is same, the recognition rate varies depending on the normalization method. A rank-level fusion method that does not require normalization is proposed. However, a existing rank-level fusion methods have lower recognition rate than score-level fusion methods. To solve this problem, we propose a rank-level fusion method with higher recognition rate than a score-level fusion method using correlation coefficient. The experiment compares recognition rate of a existing rank-level fusion methods with the recognition rate of proposed method using iris information(CASIA V3) and face information(FERET V1). We also compare with score-level fusion methods. As a result, the recognition rate improve from about 0.3% to 3.3%.

• Journal of Digital Convergence
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• v.19 no.7
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• pp.57-70
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• 2021

As COVID-19 spreads, people's interest in smart learning that can do non-face-to-face learning is increasing nowadays. In this study, we aim to empirically analyze how users' thoughts on COVID-19 and the information quality and system quality of smart learning systems affect users' acceptance of smart learning and examine the effect of perceived sensitivity and severity of COVID-19 on the satisfaction and use of smart learning through concerns about the risk of transmission. In addition, we examined the influence of information quality composed of content quality and interaction quality and system quality composed of system accessibility and functionality on the use of smart learning through user satisfaction. To verify the validity of the proposed model, we conducted a survey on 334 users with experience in using smart learning, and performed the analysis using Smart PLS 3.0. According to the analysis results, among information quality and system quality, only functionality has a positive (+) effect on the satisfaction of smart learning, and satisfaction has a positive (+) effect on the usage behavior. However, it is found that accessibility among system quality do not affect satisfaction, and concern about the risk of transmission has a negative effect on satisfaction. This study can provide meaningful guidelines to researchers when researching smart learning to support students' learning in a pandemic situation of a new infectious disease, such as COVID-19. It will also be able to provide useful implications for educational institutions and companies related to smart learning.