Privacy preserving decentralized swap derivative with deep learning based oracles leveraging blockchain technology and cryptographic primitives

【Author】 Vijayakumar, Gayathri; Singh, Kunwar; Karthika, Sk

【Source】COMPUTERS & ELECTRICAL ENGINEERING

【影响因子】4.152

【Abstract】Blockchain technology is currently revolutionizing traditional financial and business models by eliminating the need for trusted third parties. Smart contracts, a specific aspect of blockchain technology, are digital agreements that facilitate and automate processes based on the use cases. These contracts play a significant role in financial applications. Eskandari et al. (2017) have developed decentralized markets for option derivatives using Ethereum blockchain technology. To the best of our knowledge there does not exist Blockchain based Interest Rate Swap. We are the first to propose a decentralized Interest Rate Swap derivative (IRS) using Ethereum Blockchain. We have also put forth a proposal for a privacy preserving decentralized Interest Rate Swap derivative, leveraging both Ethereum Blockchain technology and Zether (B & uuml;nz et al., 2020). Furthermore, this study proposes a novel approach utilizing Long Short-Term Memory (LSTM) neural networks, a specialized form of recurrent neural networks (RNNs), to forecast interest rate swaps. We have evaluated the performance of the LSTM models using various metrics highlighting the potential for enhancing decision making processes in financial markets.

【Keywords】Derivative; Interest rate swap; Smart contracts; Ethereum blockchain

【发表时间】2024 OCT

【收录时间】2024-08-21

【文献类型】实验仿真

【主题类别】

区块链技术-平台项目-交易所