Saudi Cultural Missions Theses & Dissertations
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Item Restricted Blindly Backrunning Private Transactions With Fully Homomorphic Encryption(Imperial College London, 2024-09) Althonayan, Majed; Passerat-Palmbach, JonathanBlockchains and cryptocurrencies have experienced a monumental rise over the past decade. With Ethereum alone having around 1 million transactions per day [1], making it increasingly more attractive to opportunists who attempt to extract monetary value from transactions. This is, however, often at the expense of the user. As a result, it is of paramount importance to ensure that users are protected from malicious agents who exploit the public, transparent nature of Blockchains for individual gain. A Blockchain is a chain of blocks, each of which consisting of transactions that are executed sequentially. The ability to alter this order of transactions (by insertion, removal and re-ordering of transactions) can lead to the extraction of additional value commonly referred to as Maximal Extractable Value (MEV). MEV has led to the extraction of $750 million from Ethereum before the merge [2]. Although certain forms of MEV are universally considered to have adverse effects on users and their experience, other forms of MEV, such as arbitrage and liquidations, are believed to have a positive effect in regulating the markets. This research introduces a promising solution that allows searchers to backrun transactions, leveraging the effects of arbitrage while mitigating the harmful effects of MEV. It expands on the work done by Flashbots by utilising fully homomorphic encryption to enable the blind backrunning of transactions by searchers through the fhEVM framework [3] on the UniswapV2 decentralised exchange. This paper also addresses the challenges faced by previous works, aiming to reduce the computational overhead and enhance the solution’s usability. Despite computational constraints, this paper presents a novel solution to the outlined aims through the advancement of known solutions by allowing searchers to combine multiple transactions and accept a greater number of UniswapV2 methods, thereby allowing searchers to generate complex and novel arbitrage opportunities. This advancement is aided with use of the fhEVM framework [3] which was utilised to build and deploy the solution on the public network. This paper represents a solid foundation for future research with the aim of further enhancing the use of fully homomorphic encryption in decentralised finance to create a fairer, more ethical ecosystem.5 0Item Restricted Verification of Smart Contracts using the Interactive Theorem Prover Agda(Swansea University, 2024-07-25) Alhabardi, Fahad; Setzer, AntonThe goal of this thesis is to verify smart contracts in Blockchain. In particular, we focus on smart contracts in Bitcoin and Solidity. In order to specify the correctness of smart contracts, we use weakest preconditions. For this, we develop a model of smart contracts in the interactive theorem prover and dependent type programming language Agda and prove the correctness of smart contracts in it. In the context of Bitcoin, our verification of Bitcoin scripts consists of non-conditional and conditional scripts. For Solidity, we refer to programs using object- oriented features of Solidity, such as calling of other contracts, full recursion, and the use of gas in order to guarantee termination while having a Turing-complete language. We have developed a simulator for Solidity-style smart contracts. As a main example, we executed a reentrancy attack in our model. We have verified smart contracts in Bitcoin and Solidity using weakest precondition in Agda. Furthermore, Agda, combined with the fact that it is a theorem prover and programming language, allows the writing of verified programs, where the verification takes place in the same language in which the program is written, avoiding the problem of translation from one language to another (with possible translation mistakes).7 0Item Restricted Risk and Uncertainty in Cryptocurrency Markets(University of East Anglia, 2024-04-23) Alsamaani, Abdulrahman; Kourtis, Apostolos; Markellos, RaphaelThis dissertation consists of three kinds of research. Each one has its purpose and aim to achieve. The first research tries to discover the most effective approach for forecasting the volatility of cryptocurrency returns utilising high-frequency data that can predict the volatility of dominant and less notable cryptocurrencies. The GARCH, IGARCH, EGARCH, GJR-GARCH, HAR, and LRE models were investigated, and univariate and comprehensive regression were used. Regarding univariate regression results, the HAR model beat the other models when forecasting one day ahead, while the EGARCH model outperformed the other models when forecasting seven and thirty days ahead. In addition, the HAR + EGARCH duo beat the other model couples when forecasting one, seven, and thirty days. Aside from the primary study, the out-of-sample analysis yielded conflicting results. These results will benefit investors, portfolio managers, and other financial professionals. The second study seeks to investigate the relationship between cryptocurrency returns and uncertainty indices along with assessing the impact of the Covid-19 pandemic period on both indices and cryptocurrency returns, determining which index has the most significant influence on cryptocurrency market results, and determining which indices pair has the most significant influence on cryptocurrency market returns. Ten cryptocurrency returns, as well as eight uncertainty indices, were investigated. The Quantile Regression, Multivariate-Quantile Regression, and Granger Causality tests were used. According to the Quantile Regression results, the Cryptocurrency Policy Uncertainty index and the Cryptocurrency Price Uncertainty index considerably impact cryptocurrency returns. On the other hand, the other indices have no influence on cryptocurrency returns. The Multivariate-Quantile Regression findings demonstrated that when the cryptocurrency market experiences a bull wave, the UCRY Policy Index + Central Bank Digital Currency Attention Index combination strongly impacts cryptocurrency returns. Nonetheless, when the cryptocurrency market has a bull run, the UCRY Policy Index and the Cryptocurrency Environmental Attention (ICEA) index combination considerably impact cryptocurrency gains. During the crisis, most of the overall sample findings were verified. These insights will benefit investors, portfolio managers, and policymakers. The third research strives to find the best model for forecasting the covariance matrix of cryptocurrency returns. To achieve this purpose, five models were thoroughly examined: BEKK, Diagonal BEKK, DCC, Asymmetric DCC, and LRE are all examples of BEKK. To assess prediction accuracy and capacity, three essential criteria were used: Euclidean distance (LE), Frobenius distance (LF), and the multivariate quasi-likelihood loss function (LQ). The LRE model outperformed the other models, predicting daily and weekly frequencies more accurately. Furthermore, the Mean Squared Error (MSE) and Mean Absolute Error (MAE) loss functions were used for validation. Except for LQ, the findings were in line with the forecasting criteria. These findings have significant implications for investors and portfolio managers aiming to enhance their risk management techniques. By utilizing the knowledge provided, they may be able to make better-informed decisions to lower portfolio risk.42 0Item Restricted Corda to Ethereum Interoperability: the Use Case of Wholesale to Retail CBDC Transfer(Saudi Digital Library, 2023-12-01) Alkahtani, Munera; Vadgama, NikhilThe rapid evolution of blockchain and distributed ledger technologies (DLTs) has shown a new era of digital innovation with profound implications for various industries, particularly in the financial sector. Central to this transformation is the emergence of Central Bank Digital Currencies(CBDCs), representing a pivotal development in modernizing monetary systems. DLT has proven to be the best technology candidate for developing central bank digital currencies (CBDCs) use cases due to its many features, such as transparency and immutability. This dissertation explores the interoperability between two prominent DLTs, Corda and Ethereum, to facilitate the issuance and seamless transfer of CBDCs. The methodology employed in this research revolves around a bridge/router architecture, acting as middleware to bridge between the source DLT (Corda) and the target DLT (Ethereum). The study leverages the Cross-Blockchain Integration Design Decision (CBIDD) framework to systematically select an appropriate interoperability approach. A prototype was implemented using Node.js along with Corda and Ethereum smart contracts, utilizing the native features of Corda, Ethereum, and Web3.js. A preliminary evaluation depicts that the latency is influenced more by the DLs than the bridge components.17 0