• Journal of Korea Society of Digital Industry and Information Management
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• v.15 no.2
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• pp.29-42
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• 2019

As online services become more and more popular due to the development of IT, non-face-to-face transactions are continuously increasing rather than face-to-face transactions. The personal identity proofing service(PIPS) based on the alternative method of the resident registration number is used for the purpose of confirming the identity of the other party on the Internet. However, in the case of the current PIPS, the personal information of the PIPS user is excessively provided to the online service provider. As a result, privacy problems of online users, shortage of choice of information providing options, and lack of differentiation of authentication methods are becoming problems. Therefore, this paper proposes a method to improve the PIPS based on the current resident registration number alternative method and to provide a method to differentiate the provision of excessive personal information. In the proposed method, we analyze trends and current status of overseas online PIPS in order to provide a method of providing differentiation of personal information and proposes an effective improvement method applicable to domestic.

• Journal of Korea Society of Digital Industry and Information Management
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• v.15 no.1
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• pp.1-7
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• 2019

The objective of this study is to examine the trends in big data. Research abstracts were extracted from 4,019 articles, published between 1995 and 2018, on Web of Science and were analyzed using topic modeling and time series analysis. The 20 single-term topics that appeared most frequently were as follows: model, technology, algorithm, problem, performance, network, framework, analytics, management, process, value, user, knowledge, dataset, resource, service, cloud, storage, business, and health. The 20 multi-term topics were as follows: sense technology architecture (T10), decision system (T18), classification algorithm (T03), data analytics (T17), system performance (T09), data science (T06), distribution method (T20), service dataset (T19), network communication (T05), customer & business (T16), cloud computing (T02), health care (T14), smart city (T11), patient & disease (T04), privacy & security (T08), research design (T01), social media (T12), student & education (T13), energy consumption (T07), supply chain management (T15). The time series data indicated that the 40 single-term topics and multi-term topics were hot topics. This study provides suggestions for future research.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.978-1002
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• 2019

With the rapid development of the Internet of Things, the problem of privacy protection has been paid great attention. Recently, Nikooghadam et al. pointed out that Kumari et al.'s protocol can neither resist off-line guessing attack nor preserve user anonymity. Moreover, the authors also proposed an authentication supportive session initial protocol, claiming to resist various vulnerability attacks. Unfortunately, this paper proves that the authentication protocols of Kumari et al. and Nikooghadam et al. have neither the ability to preserve perfect forward secrecy nor the ability to resist key-compromise impersonation attack. In order to remedy such flaws in their protocols, we design a lightweight authentication protocol using elliptic curve cryptography. By way of informal security analysis, it is shown that the proposed protocol can both resist a variety of attacks and provide more security. Afterward, it is also proved that the protocol is resistant against active and passive attacks under Dolev-Yao model by means of Burrows-Abadi-Needham logic (BAN-Logic), and fulfills mutual authentication using Automated Validation of Internet Security Protocols and Applications (AVISPA) software. Subsequently, we compare the protocol with the related scheme in terms of computational complexity and security. The comparative analytics witness that the proposed protocol is more suitable for practical application scenarios.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.7
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• pp.2399-2413
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• 2021

A recommendation system is an information filter tool, which uses the ratings and reviews of users to generate a personalized recommendation service for users. However, the cold-start problem of users and items is still a major research hotspot on service recommendations. To address this challenge, this paper proposes a high-efficient hybrid recommendation system based on Fuzzy C-Means (FCM) clustering and supervised learning models. The proposed recommendation method includes two aspects: on the one hand, FCM clustering technique has been applied to the item-based collaborative filtering framework to solve the cold start problem; on the other hand, the content information is integrated into the collaborative filtering. The algorithm constructs the user and item membership degree feature vector, and adopts the data representation form of the scoring matrix to the supervised learning algorithm, as well as by combining the subjective membership degree feature vector and the objective membership degree feature vector in a linear combination, the prediction accuracy is significantly improved on the public datasets with different sparsity. The efficiency of the proposed system is illustrated by conducting several experiments on MovieLens dataset.

• The Journal of Economics, Marketing and Management
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• v.10 no.4
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• pp.13-24
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• 2022

Purpose: Although numerous research has covered content on trends in the adoption and use of wearable devices, their uses across several sectors such as healthcare, gaming, and fashion, there seems to be a considerable paucity with regard to empirical research focusing on the solutions for factors that undermine the effectiveness of wearable devices in healthcare. The present research aims to highlight what has been covered on wearable devices in healthcare while highlighting the limitations for future research. Research design, data, and methodology -The present authors conducted one of the most famous qualitative literature approach which has been called as PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) statement. The selecting criteria for eligible prior studies was estimated by whether studies are suitable for the current research, identifying they are peer-reviewed and issued by notable publishers between 2017 and 2022. Result - Our results indicated that (1) Increasing the Affordability and User Education on Wearable Devices in Healthcare (2) Tackling the Technological Issues in Wearable Devices to Promote Healthcare Delivery (3) Solving Security and Privacy Issues Associated with Wearable Devices (4) Promoting Standards and Appropriate Regulations for Wearable Devices. Conclusion - To add, resolving the technological issues associated with wearable devices in healthcare will ensure that the new devices in the market will have longer battery life, multiple functions, and enhanced accuracy, thus ensuring that patients receive better care. Necessary interventions are taken on time to avoid any deleterious consequences such as proliferating mortality rates among the different patient groups.

• International Journal of Computer Science & Network Security
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• v.22 no.7
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• pp.389-396
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• 2022

Smartphones are growing more susceptible as technology develops because they contain sensitive data that offers a severe security risk if it falls into the wrong hands. The Android OS includes permissions as a crucial component for safeguarding user privacy and confidentiality. On the other hand, mobile malware continues to struggle with permission misuse. Although permission-based detection is frequently utilized, the significant false alarm rates brought on by the permission-based issue are thought to make it inadequate. The present detection method has a high incidence of false alarms, which reduces its ability to identify permission-based attacks. By using permission features with intent, this research attempted to improve permission-based detection. However, it creates an excessive number of features and increases the likelihood of false alarms. In order to generate the optimal number of features created and boost the quality of features chosen, this research developed an intersection feature approach. Performance was assessed using metrics including accuracy, TPR, TNR, and FPR. The most important characteristics were chosen using the Correlation Feature Selection, and the malicious program was categorized using SVM and naive Bayes. The Intersection Feature Technique, according to the findings, reduces characteristics from 486 to 17, has a 97 percent accuracy rate, and produces 0.1 percent false alarms.

• International Journal of Computer Science & Network Security
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• v.22 no.7
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• pp.229-239
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• 2022

Today's Internet of Things (IoT) has had a dramatic increase in the use of various daily aspects. As a consequence, many homes adopt IoT technology to move towards the smart home. So, the home can be called smart when it has a range of smart devices that are united into one network, such as cameras, sensors, etc. While IoT smart home devices bring numerous benefits to human life, there are many security concerns associated with these devices. These security concerns, such as user privacy, can result in an insecure application. In this research, we focused on analyzing the vulnerabilities of IoT smart home cameras. This will be done by designing a new model that follows the STRIDE approach to identify these threats in order to afford an efficient and secure IoT device. Then, apply a number of test cases on a smart home camera in order to verify the usage of the proposed model. Lastly, we present a scheme for mitigation techniques to prevent any vulnerabilities that might occur in IoT devices.

• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.212-222
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• 2022

There has been a rapid increase in the use of cloud email services. As a result, email encryption has become more commonplace as concerns about cloud privacy and security grow. Nevertheless, this increase in usage is creating the challenge of how to effectively be searching and filtering the encrypted emails. They are popular technologies of solving the issue of the encrypted emails searching through searchable public key encryption. However, the problem of encrypted email filtering remains to be solved. As a new approach to finding and filtering encrypted emails in the cloud, we propose a ciphertext-based encrypted policy attribute-based encryption scheme and keyword search procedure based on hidden policy ciphertext. This feature allows the user of searching using some encrypted emails keywords in the cloud as well as allowing the emails filter-based server toward filter the content of the encrypted emails, similar to the traditional email keyword filtering service. By utilizing composite order bilinear groups, a hidden policy system has been successfully demonstrated to be secure by our dual system encryption process. Proposed system can be used with other scenarios such as searching and filtering files as an applicable method.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.161-164
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• 2022

Homomorphic Encryption (HE) schemes have been recently growing as a reliable solution to preserve users' information owe to maintaining and operating the user data in the encrypted state. In addition to that, several Neural Networks models merged with HE schemes have been developed as a prospective tool for privacy-preserving machine learning. Those mentioned works demonstrated that it is possible to match the accuracy of non-encrypted models but there is always a trade-off in the computation time. In this work, we evaluate the implementation of CKKS HE scheme operations over the layers of a LeNet5 convolutional inference model, however, owing to the limitations of the evaluation environment, the scope of this work is not to develop a complete LeNet5 encrypted model. The evaluation was performed using the MNIST dataset with Microsoft SEAL (MSEAL) open-source homomorphic encryption library ported version on Python (PyFhel). The behavior of the encrypted model, the limitations faced and a small description of related and future work is also provided.

• The Journal of Economics, Marketing and Management
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• v.12 no.1
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• pp.39-51
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• 2024

Purpose: This study aims to derive planning directions of community facilities integrating generations based on local communities to promote sustainable intergenerational exchange by analyzing the spatial configuration and programs of domestic and foreign generation-integrated community facilities based on local communities. Research design, data and methodology: Through theoretical consideration, the concept of intergenerational integration, types of intergenerational exchange, and spatial arrangement types were identified. Then, case study analysis of domestic and foreign community facilities with well-planned intergenerational exchange spaces and programs were conducted to identify intergenerational integration, and to derive community facility planning direction. Results: The results of this research are as follows. First, in terms of humanware, in order to revitalize continuous exchange between the 1st, 2nd, and 3rd generations, a systematic support system is needed to build mutual trust through voluntary participation by each generation. Second, it is important to provide a variety of shared spaces while maintaining the uniqueness of each facility from a hardware perspective, and must be planned in such a way that selective interaction takes place with privacy and interaction in mind. Third, in terms of software, programs that meet the characteristics of each user must be provided. Conclusions: It is expected that the results of this research can be used as basic data for planning community facilities that integrate generations based on local communities, contributing to the search for sustainable ways to revitalize intergenerational exchange in the future.