• Journal of the Korea Society of Computer and Information
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• v.26 no.3
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• pp.59-67
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• 2021

Blockchain technology began in 2008 when an unidentified person named Satoshi Nakamoto proposed a cryptocurrency called Bitcoin. Satoshi Nakamoto had distrust of the existing financial system and wanted to implement a financial system that is robust against hacking or mannipulation without a middleman such as a bank through blockchain technology. Satoshi proposed a blockchain as a technology to prevent the creation of the bitcoin and forging of transactions, and through this, the functions of issuance, transaction, and verification of currency were implemented. Since then, Ethereum, a cryptocurrency that can implement the smart contract on the blockchain, has been developed, allowing financial products that require complex contracts such as deposits, loans, insurance, and derivatives to be brought into the area of cryptocurrency. In addition, it is expanding the possibility of substituting products provided by financial institutions through combination with real assets. These applications are defined as Decentralized Finance (DeFi). This paper was prepared to understand the overall technical understanding of DeFi and to introduce the services currently in operation. First, the technologies and ecosystems that implement the overall DeFi are explained, and then the representative DeFi services are categorized by feature and described.

• Journal of the Korea Society of Computer and Information
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• v.26 no.2
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• pp.229-235
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• 2021

Blockchain, recognized as one of the core technologies of the 4th industrial revolution, is an Internet-based distributed data management system which does not require centralized control. Blockchain is characterized by the integrity and reliability of information, and blockchains can be used where such characteristics are required. Typical applications of blockchain include finance, transaction, and evaluation management. In this paper, we designed a blockchain-based evaluation management system that allows users to freely create and manage evaluation instances. Evaluation managers can create an evaluation instance according to their purpose and allocate evaluation items and evaluators. When the evaluators finish evaluating the evaluation items, the evaluation manager can aggregate the evaluation results for the instance. Someone, who want to perform evaluation for various purposes but do not have an evaluation system, can implement the evaluation system relatively simply by using this system. In addition, due to the characteristic of the blockchain, evaluators cannot modify the evaluation scores they have already recorded, and neither the system administrator nor the evaluation administrator can modify the evaluation scores. For this reason, the reliability of the evaluation increases.

• Journal of Industrial Convergence
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• v.18 no.6
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• pp.147-154
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• 2020

It is difficult to educate the overall operation of public and private blockchains with different characteristics. Recently, most education for blockchain is targeted at public blockchains such as Bitcoin and Ethereum. However, in an actual business environment, a private blockchain such as HyperLedger Fabric is used because access to corporate data is controlled through user authentication. In the case of HLF-based education, it is necessary to understand various components that are not in the public blockchain, such as peers, orderers, and channels. In this paper, a lab framework for HLF is designed for an efficient and systematic understanding of the functions and operations. The framework consists of HLF network, chaincode, and decentralized software control functions. Through the framework, the network configuration, distribution and activation of chaincode, and dApp execution process were checked step by step, and it was very easy to understand the overall flow for blockchain services. In addition, it is expected that a systematic understanding of the overall flow will be possible even in future network expansion.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.6
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• pp.1157-1169
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• 2021

The disaster recovery refers to policies and procedures to ensure continuity of services and minimize loss of resources and finances in case of emergency situations such as natural disasters. In particular, the disaster recovery method by the cloud service provider has advantages such as management flexibility, high availability, and cost effectiveness. However, this method has a dependency on a service provider and has a structural limitation in which a user cannot be involved in personal data. In this paper, we propose a protocol using proxy re-encryption for data confidentiality by removing dependency on service providers by backing up user data using blockchain and distributed storage. The proposed method is implemented in Ethereum and IPFS environments, and presents the performance and cost required for backup and recovery operations.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.5
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• pp.793-801
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• 2022

Most Bluetooth devices are commonly used in various ways these days, but they can be often lost due to small-size devices. However, most Bluetooth protocol do not provide authentication functions to legitimate owners, and thus someone who obtains the lost Bluetooth device can easily connect to their smart devices to use it. In this paper, we propose NBAS can authenticates legitimate owners using NFT on lossy Bluetooth devices.NBAS generates a digital wallet on the blockchain using the decentralized network Ethereum blockchain and facilitating the MAC address of the Bluetooth device in the digital wallet. The owner of the wallet uses a private key to certify the Bluetooth device using NFT. The initial pairing time of NBAS was 10.25 sec, but the reconnection time was 0.007 sec similar to the conventional method, and the pairing rejection time for unapproved users was 1.58 sec on average. Therefore, the proposed NBAS effectively shows the device authentication over the conventional Bluetooth.

• Journal of the Korea Society of Computer and Information
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• v.28 no.2
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• pp.47-53
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• 2023

In this paper, based on the generalization and popularization of NFT, the utilization idea of using NFT in e-sports was proposed. We considered ways to utilize NFTs to make access to e-sports easy for all users and to secure users from various age groups. To this end, cases of NFTs with diversity in e-sports platforms were analyzed by type, and the degree of use of NFTs in e-sports was identified through a survey. As a result of the study, it was found that the NFT experience in the e-sports game was highly satisfactory and the desire to experience it again was strong. As NFTs have ownership and scarcity as important characteristics, they can respond well to the demand for owning unique items in e-sports. In addition, in marketing, by promoting limited edition products with scarcity, it is possible to promote marketing that creates value with high profitability. When using NFT in e-sports, various NFT functions are combined regardless of the type of sport, so NFT can become an economic infrastructure.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.2
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• pp.51-58
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• 2022

Blockchain technology is a system in which data from users participating in blockchain networks is distributed and stored. Bitcoin and Ethereum are attracting global attention, and the utilization of blockchain is expected to be endless. However, the need for blockchain data privacy protection is emerging in various financial, medical, and real estate sectors that process personal information due to the transparency of disclosing all data in the blockchain to network participants. Although studies using smart contracts, homomorphic encryption, and cryptographic key methods have been mainly conducted to protect existing blockchain data privacy, this paper proposes data privacy using matrix character relocation techniques differentiated from existing papers. The approach proposed in this paper consists largely of two methods: how to relocate the original data to matrix characters, how to return the deployed data to the original. Through qualitative experiments, we evaluate the safety of the approach proposed in this paper, and demonstrate that matrix character relocation will be sufficiently applicable in private blockchain environments by measuring the time it takes to revert applied data to original data.

• Journal of the Korea Society of Computer and Information
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• v.28 no.5
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• pp.49-56
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• 2023

Currently, e-commerce is changing from a digital twin to a metaverse world. The metaverse world is an intermediate form between virtual and real worlds and is operated as a coin-based meta-commerce. In this meta-commerce world, blockchain-based NFT coins are used when trading items (contents). In this study, we studied how to issue a large number of NFT coins (certification) rather than issuing a single type of NFT. The research method was designed to produce content layer-based and automatically create the desired quantity using a mass NFT index and automatic generation method. In this study, a layer overlap method (background, body, etc.) was used with a Phyton-based program for mass minting. As a result, it can be used as a blockchain-based certificate that can prove a group of many people. In addition, the content created with the NFT index was registered on the NFT sales site to confirm its utilization and value.

• Journal of Convergence for Information Technology
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• v.12 no.3
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• pp.116-125
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• 2022

The purpose of this study is to analyze whether investor anxiety caused by COVID-19 affects cryptocurrency prices in the COVID-19 pandemic, and to experiment with cryptocurrency price prediction based on a deep learning model. Investor anxiety is calculated by combining Naver's Corona search index and Corona confirmed information, analyzing Granger causality with cryptocurrency prices, and predicting cryptocurrency prices using deep learning models. The experimental results are as follows. First, CCI indicators showed significant Granger causality in the returns of Bitcoin, Ethereum, and Lightcoin. Second, LSTM with CCI as an input variable showed high predictive performance. Third, Bitcoin's price prediction performance was the highest in comparison between cryptocurrencies. This study is of academic significance in that it is the first attempt to analyze the relationship between Naver's Corona search information and cryptocurrency prices in the Corona phase. In future studies, extended studies into various deep learning models are needed to increase price prediction accuracy.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.4
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• pp.152-159
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• 2023

This study endeavors to enrich investment prospects in cryptocurrency by establishing a rationale for investment decisions. The primary objective involves evaluating the predictability of four prominent cryptocurrencies - Bitcoin, Ethereum, Litecoin, and EOS - and scrutinizing the efficacy of trading strategies developed based on the prediction model. To identify the most effective prediction model for each cryptocurrency annually, we employed three methodologies - AutoRegressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), and Prophet - representing traditional statistics and artificial intelligence. These methods were applied across diverse periods and time intervals. The result suggested that Prophet trained on the previous 28 days' price history at 15-minute intervals generally yielded the highest performance. The results were validated through a random selection of 100 days (20 target dates per year) spanning from January 1st, 2018, to December 31st, 2022. The trading strategies were formulated based on the optimal-performing prediction model, grounded in the simple principle of assigning greater weight to more predictable assets. When the forecasting model indicates an upward trend, it is recommended to acquire the cryptocurrency with the investment amount determined by its performance. Experimental results consistently demonstrated that the proposed trading strategy yields higher returns compared to an equal portfolio employing a buy-and-hold strategy. The cryptocurrency trading model introduced in this paper carries two significant implications. Firstly, it facilitates the evolution of cryptocurrencies from speculative assets to investment instruments. Secondly, it plays a crucial role in advancing deep learning-based investment strategies by providing sound evidence for portfolio allocation. This addresses the black box issue, a notable weakness in deep learning, offering increased transparency to the model.