• Convergence Security Journal
• /
• v.20 no.2
• /
• pp.15-28
• /
• 2020

Smart City is highly anticipated to solve the problems of existing cities and create new added value, but there is also increasing concern about security risks. The negative view of smart city according to security risk awareness is a problem that needs to be improved in order to activate the fourth industrial revolution technology and develop smart city. This study examined risk factors in smart cities based on perceived risk and user resistance theory, and empirically analyzed the relationship with resistance attitudes. According to the empirical analysis with 288 research samples, security, social, and physical risk factors directly affect smart city resistance, while financial, performance, and privacy risk have no significant effect. In addition, it was verified that the security risk can is an antecedent factor for other risk factors, and it was confirmed that it is required to separately discuss the security and privacy risk in the smart city environment. This study shows that it is necessary to prepare policy supports for social interactions as well as security and physical safety issues in order to activate smart city by discussing the risk factors that negatively affect smart city perception from the public's point of view.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.29 no.3
• /
• pp.565-577
• /
• 2019

In recent years, biometric authentication has been used for various applications. Since biometric features are unchangeable and cannot be revoked unlike other personal information, there is increasing concern about leakage of biometric information. Recently, Jin et al. proposed a new cancelable biometric scheme, called "Index-of-Max" (IoM) to protect fingerprint template. The authors presented two realizations, namely, Gaussian random projection-based and uniformly random permutation-based hashing schemes. They also showed that their schemes can provide high accuracy, guarantee the security against recently presented privacy attacks, and satisfy some criteria of cancelable biometrics. However, the authors did not provide experimental results for other biometric features (e.g. finger-vein, iris). In this paper, we present the results of applying Jin et al.'s scheme to iris data. To do this, we propose a new method for processing iris data into a suitable form applicable to the Jin et al.'s scheme. Our experimental results show that it can guarantee favorable accuracy performance compared to the previous schemes. We also show that our scheme satisfies cancelable biometrics criteria and robustness to security and privacy attacks demonstrated in the Jin et al.'s work.

• The Journal of the Convergence on Culture Technology
• /
• v.7 no.1
• /
• pp.595-607
• /
• 2021

In response to increasing demand of contactless services, the overall usage of "task-oriented chatbots" in the industry is on the rise. The purpose of a task-oriented chatbot is to raise the efficiency of data sharing and workflow; in order to establish a guideline, there must be a discussion on "what" and "how" to share information. We investigate the effects of personalization and different types of the interface on 'performance expectancy', 'effort expectancy', 'intention to use', and 'satisfaction' in the context of a task-oriented chatbot. Results show that 'intention to use' and 'satisfaction' were higher when the level of personalization was higher. Within the closed-discourse interface, 'intention to use' and 'satisfaction' were higher when personalization was lower. We highlight the practical insights in the use of personalization and types of chatbot interface based on 'perceived personalization', 'expectation disconfirmation theory', 'privacy concern' and 'privacy paradox'.

• International Journal of Computer Science & Network Security
• /
• v.22 no.3
• /
• pp.303-311
• /
• 2022

The tremendous growth of the Internet of things is unbelievable. Many IoT devices have emerged on the market over the last decade. This has made our everyday life easier inside our homes. The technology used at home has changed significantly over the past several decades, leading to what is known today as the smart home. However, this growth has also brought new challenges to our home security and privacy. With the smart home becoming more mainstream, cybersecurity issues have become a fundamental concern. The smart home is an environment where heterogeneous devices and appliances are interconnected through the Internet of Things (IoT) to provide smart services to residents. These services include home climate control, energy management, video on demand, music on-demand, remote healthcare, remote control, and other similar services in a ubiquitous manner. Smart home devices can be controlled via the Internet using smartphones. However, connecting smart home appliances to wireless networks and the Internet makes individuals vulnerable to malicious attacks. Remote access within the same environment or over the Internet requires an effective access control mechanism. This paper intends to shed light on how smart home devices are working as well as the type of security and privacy threats of the smart home. It also illustrated the types of authentication methods that can be used with smart home devices. In addition, a comparison of Smart home IoT-based security protocols was presented along with a security countermeasure that can be used in a smart home environment. Finally, a few open problems were mentioned as future research directions for researchers.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.16 no.2
• /
• pp.742-756
• /
• 2022

A flood of information has occurred with the rise of the internet and digital devices in the fourth industrial revolution era. Every millisecond, massive amounts of structured and unstructured data are generated; smartphones, wearable devices, sensors, and self-driving cars are just a few examples of devices that currently generate massive amounts of data in our daily. Machine learning has been considered an approach to support and recognize patterns in data in many areas to provide a convenient way to other sectors, including the healthcare sector, government sector, banks, military sector, and more. However, the conventional machine learning model requires the data owner to upload their information to train the model in one central location to perform the model training. This classical model has caused data owners to worry about the risks of transferring private information because traditional machine learning is required to push their data to the cloud to process the model training. Furthermore, the training of machine learning and deep learning models requires massive computing resources. Thus, many researchers have jumped to a new model known as "Federated Learning". Federated learning is emerging to train Artificial Intelligence models over distributed clients, and it provides secure privacy information to the data owner. Hence, this paper implements Federated Averaging with a Deep Neural Network to classify the handwriting image and protect the sensitive data. Moreover, we compare the centralized machine learning model with federated averaging. The result shows the centralized machine learning model outperforms federated learning in terms of accuracy, but this classical model produces another risk, like privacy concern, due to the data being stored in the data center. The MNIST dataset was used in this experiment.

• Knowledge Management Research
• /
• v.15 no.2
• /
• pp.89-106
• /
• 2014

As an empirical study on the psychological side effects of using Social Networking Services (SNS), this study aims to identify the reality of negative emotion of using SNS and to predict its consequences. To this end, a measurement tool was developed through literature review, in-depth interview with users and expert review to induce negative emotional factors that can arise while using SNS. An exploratory factor analysis was performed for a total of 24 measurement items, which then were divided into the following 6 factors: 'concern over privacy,' 'burden from undesired connection,' 'relative deprivation,' 'a sense of alienation,' 'concern over reputation' and 'negative feeling about simple relationship.' Also, the relationship between the 6 negative emotional factors and psychological dissonance was analyzed. The results indicate that all the factors, except relative deprivation and a sense of alienation, affect psychological dissonance. It was also found that psychological dissonance, which implies a conflicting condition from using SNS, significantly affects the behavior that possibly reduces and limits the use of SNS. In other words, the users who have experienced psychological dissonance respond passively by avoiding the use of SNS to resolve the dissonance. The results of this study provide the base for explaining the psychological side effects of using SNS, which have been understood at a phenomenal level, such as 'Facebook depression' or 'SNS stress.' In addition, this study is of significance as it helps understand the psychological mechanism by identifying the relationship between negative emotion and use behavior with the theory of cognitive dissonance.

• Knowledge Management Research
• /
• v.21 no.4
• /
• pp.211-225
• /
• 2020

We investigate factors affecting chatbot use intention of online shopping mall users. We identify theoretical foundations from the literature and postulate that accuracy, personalization level, intelligence, intimacy, social presence, and piracy concern should affect intention to use more or negative intention to use. Based on 300 responses from online shopping mall chatbot users in Korea, we run the statistical analysis to assure the reliability and validity of the measurements. From the multiple regression analysis, we find that personalization level, intelligence, social presence, and privacy concerns significantly affect intention to use more. In contrast, we find that accuracy and privacy concerns significantly affect negative intention to use. This work will present pragmatic implications upon the design and management of chatbot in order to not only incent customers to use more but reduce factors that may cause negative use intention. Among functional factors, personalization and intelligence increases the intention to use more while accuracy decreases negative intention to use. Among emotional factors such as intimacy and social presence, we find that only social presence significantly increases intention to use more. Privacy concerns is found to decrease intention to use and increase negative intention to use.

• KIPS Transactions on Computer and Communication Systems
• /
• v.9 no.12
• /
• pp.291-306
• /
• 2020

Nowadays, Data-Network-AI (DNA)-based intelligent services and applications have become a reality to provide a new dimension of services that improve the quality of life and productivity of businesses. Artificial intelligence (AI) can enhance the value of IoT data (data collected by IoT devices). The internet of things (IoT) promotes the learning and intelligence capability of AI. To extract insights from massive volume IoT data in real-time using deep learning, processing capability needs to happen in the IoT end devices where data is generated. However, deep learning requires a significant number of computational resources that may not be available at the IoT end devices. Such problems have been addressed by transporting bulks of data from the IoT end devices to the cloud datacenters for processing. But transferring IoT big data to the cloud incurs prohibitively high transmission delay and privacy issues which are a major concern. Edge computing, where distributed computing nodes are placed close to the IoT end devices, is a viable solution to meet the high computation and low-latency requirements and to preserve the privacy of users. This paper provides a comprehensive review of the current state of leveraging deep learning within edge computing to unleash the potential of IoT big data generated from IoT end devices. We believe that the revision will have a contribution to the development of DNA-based intelligent services and applications. It describes the different distributed training and inference architectures of deep learning models across multiple nodes of the edge computing platform. It also provides the different privacy-preserving approaches of deep learning on the edge computing environment and the various application domains where deep learning on the network edge can be useful. Finally, it discusses open issues and challenges leveraging deep learning within edge computing.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.25 no.5
• /
• pp.1245-1255
• /
• 2015

As cloud computing services become popular, the concern on the data security of cloud services increases and the efforts for the data security become essential. In this paper, we describe the pros and cons of cloud computing including the definition of cloud. Then, we discuss the regulations about the protection of user data defined in cloud promotion act. Previous studies related to the privacy protection and the entrustment of personal information in cloud computing are reviewed. We examine how to store the personal information depending on the cloud service model. As a result, we argue that the entrustment of personal information should vary according to the cloud service model and we propose how to protect the personal information on IaaS and SaaS cloud service models.

• Journal of Global Scholars of Marketing Science
• /
• v.7
• /
• pp.321-337
• /
• 2001

The Purpose of this study is to empirically examine the factors that affect the consumer's buying behavior under the e-commerce environment. In order to achieve this goal, vendor characteristics, securities of transaction, concern for privacy, shopping orientation and perceived channel utilities were used as independent variables. Findings of study indicated that the concerns for abusing individual information, perceived securities of transaction, consumer's recreational orientation, consumer's convenience orientation, perceived distribution channel are the robust predictors of buying behavior of internet users.