• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.31 no.4
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• pp.295-303
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• 2021

Objective: Paint manufacturing industry workers are exposed to various lung cancer carcinogenic substances including hexavalent chromium and crystalline silica. Studies have been conducted on lung cancer in Paint manufacturing industry workers and the concentration of hexavalent chromium in paint industry; however, the concentration of crystalline silica and hexavalent chromium and cases of lung cancer in a single Paint factory has never been reported in Korea. Methods: To determine whether the cancer was related to his work environment, we assessed the level of exposure to carcinogens during pouring and mixing talc and pigment. In addition, a mask fit test was performed for the worker. Results: Analysis of talc and silica bulk powder materials showed that crystalline silica (quartz) was 5% in talc and 100% in silica. The green and yellow pigments contained 87% and 92% of lead chromate, respectively. Our quantitative analysis of pigment powder samples showed that the hexavalent chromium contents quantified in the green and yellow pigment samples were 87% and 92%, respectively. In order to estimate his exposure level of hexavalent chromium, we measured a personal exposure level of hexavalent chromium for a worker in accordance with the National Institute for Occupational Safety and Health #7605 method. The results showed that the worker was exposed to the high level of hexavalent chromium (0.033 mg m-3). In addition, the talc powder also contained 5% quartz, and the worker's exposure level to respirable quartz exceeded OEL. As a result of the respiratory protection fit test for workers, the overall Fit Factor was '15' when wearing a second-grade mask and '25' when wearing a first-grade mask, significantly lower than the US Occupational Safety and Health Agency (OSHA) pass value of "100". Conclusion: Workers who pouring and mixing powder materials such as talc or colored pigments in paint manufacturing company may be exposed to high concentrations of carcinogenic substances. These findings indicate that it is necessary to local ventilation system inspection, safety and health education for employers and workers, and periodically monitoring and manage the working environment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.8
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• pp.2827-2848
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• 2021

Mobile Crowdsourcing (MCS) has become an emerging paradigm evolved from crowdsourcing by employing advanced features of mobile devices such as smartphones to perform more complicated, especially spatial tasks. One of the key procedures in MCS is to collect answers from mobile users (workers), which may face several security issues. First, authentication is required to ensure that answers are from authorized workers. In addition, MCS tasks are usually location-dependent, so the collected answers could disclose workers' location privacy, which may discourage workers to participate in the tasks. Finally, the overhead occurred by authentication and privacy protection should be minimized since mobile devices are resource-constrained. Considering all the above concerns, in this paper, we propose a lightweight and privacy-preserving answer collection scheme for MCS. In the proposed scheme, we achieve anonymous authentication based on traceable ring signature, which provides authentication, anonymity, as well as traceability by enabling malicious workers tracing. In order to balance user location privacy and data availability, we propose a new concept named current location privacy, which means the location of the worker cannot be disclosed to anyone until a specified time. Since the leakage of current location will seriously threaten workers' personal safety, causing such as absence or presence disclosure attacks, it is necessary to pay attention to the current location privacy of workers in MCS. We encrypt the collected answers based on timed-release encryption, ensuring the secure transmission and high availability of data, as well as preserving the current location privacy of workers. Finally, we analyze the security and performance of the proposed scheme. The experimental results show that the computation costs of a worker depend on the number of ring signature members, which indicates the flexibility for a worker to choose an appropriate size of the group under considerations of privacy and efficiency.

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

• Journal of Digital Convergence
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• v.17 no.7
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• pp.285-292
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• 2019

With the popularization of PC, SNS and IoT, a lot of data is generated and the amount is increasing exponentially. Artificial neural network learning is a topic that attracts attention in many fields in recent years by using huge amounts of data. Artificial neural network learning has shown tremendous potential in speech recognition and image recognition, and is widely applied to a variety of complex areas such as medical diagnosis, artificial intelligence games, and face recognition. The results of artificial neural networks are accurate enough to surpass real human beings. Despite these many advantages, privacy problems still exist in artificial neural network learning. Learning data for artificial neural network learning includes various information including personal sensitive information, so that privacy can be exposed due to malicious attackers. There is a privacy risk that occurs when an attacker interferes with learning and degrades learning or attacks a model that has completed learning. In this paper, we analyze the attack method of the recently proposed neural network model and its privacy protection method.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.85-110
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• 2018

A chatbot is an interactive assistant that utilizes many communication modes: voice, images, video, or text. It is an artificial intelligence-based application that responds to users' needs or solves problems during user-friendly conversation. However, the current version of the chatbot is focused on understanding and performing tasks requested by the user; its ability to generate personalized conversation suitable for relationship-building is limited. Recognizing the need to build a relationship and making suitable conversation is more important for social chatbots who require social skills similar to those of problem-solving chatbots like the intelligent personal assistant. The purpose of this study is to propose a text analysis method that evaluates relationships between chatbots and users based on content input by the user and adapted to the communication situation, enabling the chatbot to conduct suitable conversations. To evaluate the performance of this method, we examined learning and verified the results using actual SNS conversation records. The results of the analysis will aid in implementation of the social chatbot, as this method yields excellent results even when the private profile information of the user is excluded for privacy reasons.

• Journal of Korean Society of Disaster and Security
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• v.11 no.2
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• pp.75-82
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• 2018

The purpose of this study is designed to apply the model of the problem-based learning in the class of Disaster Psychology and then analyze the experiences that its students felt. The participants in this research are 56 undergraduates. The class of Disaster Psychology was conducted with blended learning using lecture and PBL. The PBL problem should be solved just for 3 weeks. The data collected after the class is an analysis of the PBL problem, log on group activities, personal reflection diary, Group evaluation. Then, each data should be collected and analyzed quantitatively through the repetitive comparison, and the triangle-measurement. The findings suggest that there is a remarkable educational learning experience in seven categories: acquire expertise, confidence, practical problem-solving skill, communication ability, roles of calling, efficacy, change in perspective. This study introduces a case of PBL course development and expects subsequent applications and research.

• Journal of Convergence for Information Technology
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• v.9 no.7
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• pp.14-24
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• 2019

It is necessary to look into multiple subjects, such as effectiveness, laws and polices of blockchain in order to easily accept blockchain technology in small and mid-sized enterprises(SME). This study analyzes the positive effects of applying the block chain to SMEs, examines the laws and policies required to apply them, and identifies the tasks. As a result, we confirmed that it can create positive effects such as optimizing supply chain management, simplifying import and export process documents, improving product quality, facilitating flow of funds, and improving transaction reliability. Also, we confirmed that it is necessary to improve the basic law of electronic transaction, electronic commerce law, electronic financial transaction law, personal information protection legislation, and needs policy supplement for platform development, education system for SMEs, transaction standardization guidelines, tax reduction policy, and block chain research and development. More extensive practical research and specific individual legal studies are needed in the future.

• The Journal of Bigdata
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• v.3 no.2
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• pp.1-10
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• 2018

In the field of smart water management, there is an increasing demand for strengthening competitiveness through big data analysis. As a result, systematic management (Governance) of big data is becoming an important issue. Big data governance is a systematic approach to evaluating, directing and monitoring data management, such as data quality assurance, privacy protection, data lifetime management, data ownership and clarification of management rights. Failure to establish big data governance can lead to serious problems by using low quality data for critical decisions. In addition, personal privacy data can make Big Brother worry come true, and IT costs can skyrocket due to the neglect of data age management. Even if these technical problems are fixed, the big data effects will not be sustained unless there are organizations and personnel who are dedicated and responsible for data-related issues. In this paper, we propose a method of building data governance for smart water data management based on big data.

• Journal of the Korean Society of Systems Engineering
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• v.14 no.2
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• pp.73-82
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• 2018

In this work, a hardware based cryptographic module for the cyber security of nuclear power plant is developed using a system engineering approach. Nuclear power plants are isolated from the Internet, but as shown in the case of Iran, Man-in-the-middle attacks (MITM) could be a threat to the safety of the nuclear facilities. This FPGA-based module does not have an operating system and it provides protection as a firewall and mitigates the cyber threats. The encryption equipment consists of an encryption module, a decryption module, and interfaces for communication between modules and systems. The Advanced Encryption Standard (AES)-128, which is formally approved as top level by U.S. National Security Agency for cryptographic algorithms, is adopted. The development of the cyber security module is implemented in two main phases: reverse engineering and re-engineering. In the reverse engineering phase, the cyber security plan and system requirements are analyzed, and the AES algorithm is decomposed into functional units. In the re-engineering phase, we model the logical architecture using Vitech CORE9 software and simulate it with the Enhanced Functional Flow Block Diagram (EFFBD), which confirms the performance improvements of the hardware-based cryptographic module as compared to software based cryptography. Following this, the Hardware description language (HDL) code is developed and tested to verify the integrity of the code. Then, the developed code is implemented on the FPGA and connected to the personal computer through Recommended Standard (RS)-232 communication to perform validation of the developed component. For the future work, the developed FPGA based encryption equipment will be verified and validated in its expected operating environment by connecting it to the Advanced power reactor (APR)-1400 simulator.

• Health Policy and Management
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• v.31 no.3
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• pp.244-254
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• 2021

The management of emerging infectious diseases cannot help but completely depend on non-pharmaceutical interventions in the early stages of the outbreak. Consequently, South Korea has developed and implemented the 3T (test-trace-treat) models, non-pharmaceutical infection prevention and control (IPC) measures, in response to the coronavirus disease 2019 (COVID-19) pandemic. The IPC measures have gained global attention, rendering them to be essential in the development of a shareable, reusable, and applicable protocol for future pandemics. This study was conducted to identify the requirements necessary for standardizing the IPC measures. Three new work items of the 18 3T models were proposed to ISO/TC 304 (International Organization for Standardization/Technical Committee 304; healthcare organization management). Requirements for each IPC measure, identified by participating members (P-members) countries during the ISO ballots, were analyzed in this study. The three new work items were approved by the P-members countries after a 3-month ballot. There was a consensus that the three IPC measure models should be International Standards (IS). Other comments include (1) the models should include not only COVID-19 but also any respiratory pandemic; and (2) keep donning of level D protection at screening sites as an optional protocol, in consideration for the lack of personal protective equipment. Standardization is a systematic process of developing internationally agreed-upon wisdom and knowledge that consider and respect the diversity and universality of each country. It is expected that such standardized applicable IPC measure models contribute to global efforts to rapidly respond to a public health emergency of international concern during its early stages.