• Review of KIISC
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• v.34 no.1
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• pp.33-36
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• 2024

본 연구에서는 퍼블릭 블록체인 기술을 적용하여 탈중앙화된 식별 정보(DID) 어플리케이션을 개발하였다. 기존의 컨소시엄 블록체인을 사용한 DID는 일부 참가자들 간의 신뢰를 기반으로 동작하여 특정 그룹의 중앙화된 통제 가능성으로 인해 보안 문제가 발생하는데, 이를 극복하기 위해 퍼블릭 블록체인을 도입하여 안전하고 투명한 디지털 식별 체계를 제시한다. 또한, 본 연구에서는 웹앱 형태의 DID 어플리케이션을 사용자 친화적이고 직관적인 디자인으로 구현하여 사용자들이 쉽게 식별 정보를 관리하고 교환할 수 있도록 하였다. 더불어 안전한 키 관리 및 전자 서명 기술을 적용하여 사용자들은 자신의 식별 정보를 완전히 통제할 수 있으며, 이를 통해 탈중앙화된 신뢰 모델을 체험할 수 있다.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.8
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• pp.269-280
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• 2022

With the rise of a decentralized finance market (so called, DeFi) using blockchain technology, users and capital liquidity of decentralized finance applications are increasing significantly. The Automated Market Maker (AMM) is a protocol that automatically calculates the asset price based on the liquidity of the decentralized trading platform, and is currently most commonly used in the decentralized exchanges (DEX), since it can proceed the transactions by utilizing the liquidity pool of the trading platform even if the buyers and sellers do not exist at the same time. However, Automated Market Maker have some disadvantages since the cost efficiency of each transaction using Automated Market Maker depends on the liquidity size of some liquidity pools used for the transaction, so the smaller the size of the liquidity pool and the larger the transaction size, the smaller the cost efficiency of the trade. To solve this problem, some platforms are adopting Transaction Path Routing Algorithm that bypasses transaction path to other liquidity pools that have relatively large size to improve cost efficiency, but this algorithm can be further improved because it uses only a single transaction path to proceed each transaction. In addition to just bypassing transaction path, in this paper we proposed a Multi-Path Routing Algorithm that uses multiple transaction paths simultaneously by distributing transaction size, and showed that the cost efficiency of transactions can be further improved in the Automated Market Maker-based trading environment.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.05a
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• pp.182-185
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• 2019

최근 다양한 보안 사고들이 발생함에 따라 보안의 중요성이 대두되고 분야를 가리지 않고 일어나는 보안 이슈에 대응하기 위한 개발이 다양화되고 있다. 이 중 블록체인 기반 암호화폐의 일종인 이더리움(Ethereum)의 탈중앙화 어플리케이션(DApp) 시스템이 이슈화되었다. DApp을 이용하여 개발된 취약점 분산 관리 솔루션은 DApp의 기능과 장점을 포함한다. 취약점 분산관리 솔루션은 현 시대의 데이터 관리 구조의 문제점을 해결하기 위한 대책으로 제시되며 필요성을 드러낸다. 또한, 기존의 중앙 집중형 시스템에서 벗어나 솔루션의 이점을 활용하여 사용 영역을 확장할 수 있다.

• 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.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.12
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• pp.321-332
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• 2020

Decentralized approaches are extensively researched by academia and industry in order to cover up the flaws of existing systems in terms of data privacy. Blockchain and decentralized learning are prominent representatives of a deconcentrated approach. Blockchain is secure by design since the data record is irrevocable, tamper-resistant, consensus-based decision making, and inexpensive of overall transactions. On the other hand, decentralized learning empowers a number of devices collectively in improving a deep learning model without exposing the dataset publicly. To motivate participants to use their resources in building models, a decent and proportional incentive system is a necessity. A centralized incentive mechanism is likely inconvenient to be adopted in decentralized learning since it relies on the middleman that still suffers from bottleneck issues. Therefore, we design an incentive model for decentralized learning applications by leveraging the Ethereum smart contract. The simulation results satisfy the design goals. We also outline the concerns in implementing the presented scheme for sensitive data regarding privacy and data leakage.

• 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.