Smart contracts are a secure and trustworthy application that plays a vital role in decentralized applications in various fields such as insurance,the internet, and gaming. However, in recent years, smart contract security breaches have occurred frequently, and due to their financial properties, they have caused huge economic losses, such as the most famous security incident "The DAO" which caused a loss of over $60 million in Ethereum. This has drawn a lot of attention from all sides. Writing a secure smart contract is now a critical issue. This paper focuses on Ether smart contracts and explains the main components of Ether, smart contract architecture and mechanism. The environment used in this paper is the Ethernet environment, using remix online compilation platform and Solidity language, according to the four security events of American Chain, The DAO, Parity and KotET, the principles of integer overflow attack, reentrant attack, access control attack and denial of service attack are studied and analyzed accordingly, and the scenarios of these vulnerabilities are reproduced, and the measures to prevent them are given. Finally, preventive measures are given. In addition, the principles of short address attack, early transaction attack and privileged function exposure attack are also introduced in detail, and security measures are proposed. As vulnerabilities continue to emerge, their classification will also evolve. The analysis and research of the current vulnerabilities are also to lay a solid foundation for avoiding more vulnerabilities.
Justin A. Drake, Dmitry Khovratovich, Mikhail Kudinov, Benedikt Wagner
With the threat posed by quantum computers on the horizon, systems like Ethereum must transition to cryptographic primitives resistant to quantum attacks. One of the most critical of these primitives is the non-interactive multi-signature scheme used in Ethereum's proof-of-stake consensus, currently implemented with BLS signatures. This primitive enables validators to independently sign blocks, with their signatures then publicly aggregated into a compact aggregate signature. In this work, we introduce a family of hash-based signature schemes as post-quantum alternatives to BLS. We consider the folklore method of aggregating signatures via (hash-based) succinct arguments, and our work is focused on instantiating the underlying signature scheme. The proposed schemes are variants of the XMSS signature scheme, analyzed within a novel and unified framework. While being generic, this framework is designed to minimize security loss, facilitating efficient parameter selection. A key feature of our work is the avoidance of random oracles in the security proof. Instead, we define explicit standard model requirements for the underlying hash functions. This eliminates the paradox of simultaneously treating hash functions as random oracles and as explicit circuits for aggregation. Furthermore, this provides cryptanalysts with clearly defined targets for evaluating the security of hash functions. Finally, we provide recommendations for practical instantiations of hash functions and concrete parameter settings, supported by known and novel heuristic bounds on the standard model properties.
José Juan de León, Cenchuan Zhang, Christos - Spyridon Koulouris, Francesca Medda · 5 authors
The growing interest in decentralized finance (DeFi), driven by advancements in blockchain technologies such as Ethereum, highlights the crucial role of smart contracts. However, the inherent openness of blockchains creates an extensive attack surface, exposing participants’ funds to undetected security flaws. In this work we investigated the use of deep reinforcement learning techniques, specifically Deep Q-Network (DQN) and Proximal Policy Optimization (PPO), for detecting and classifying vulnerabilities in smart contracts. This approach utilizes control flow graphs (CFGs) generated through EtherSolve to capture the semantic features of contract bytecode, enabling the reinforcement learning models to recognize patterns and make more accurate predictions. Experimental results from extensive public datasets of smart contracts revealed that the PPO model performs better than DQN and demonstrates effectiveness in identifying unchecked-call vulnerability. The PPO model exhibits more stable and consistent learning patterns and achieves higher overall rewards. This research introduces a machine learning method for enhancing smart contract security, reducing financial risks for users, and contributing to future developments in reinforcement learning applications.
Context: Smart contract vulnerabilities pose significant security risks for the Ethereum ecosystem, driving the development of automated tools for detection and mitigation. Smart contracts are written in Solidity, a programming language that is rapidly evolving to add features and improvements to enhance smart contract security. New versions of Solidity change the compilation process, potentially affecting how tools interpret and analyze smart contract code. Objective: In such a continuously evolving landscape, we aim to investigate the compatibility of detection tools with Solidity versions. More specifically, we present a plan to study detection tools by empirically assessing (i) their compatibility with the Solidity pragma directives, (ii) their detection effectiveness, and (iii) their execution time across different versions of Solidity. Method: We will conduct an exploratory study by running several tools and collecting a large number of real-world smart contracts to create a balanced dataset. We will track and analyze the tool execution through SmartBugs, a framework that facilitates the tool execution and allows the integration of new tools.
Blockchain solutions typically assume a synchronous network to ensure consistency and achieve consensus. In contrast, offline transaction systems aim to enable users to agree on and execute transactions without assuming bounded communication delays when interacting with the blockchain. Most existing offline payment schemes depend on trusted hardware wallets that are assumed to be secure and tamper-proof. While this work introduces Overdraft, a novel offline payment system that shifts the reliance from hardware to users themselves. Overdraft allows potential payment receivers to assess the likelihood of being paid, allowing them to accept transactions with confidence or deny them. Overdraft achieves this by maintaining a loan network that is weighted by online reputation. This loan network contains time-limited agreements where users pledge to cover another user's payment if necessary. For example, when a payer lacks sufficient funds at the moment of commitment. Offline users rely on the last known view of the loan network -- which they had access to when last online -- to determine whether to participate in an offline transaction. This view is used to estimate the probability of eventual payment, possibly using multiple loans. Once online again, users commit their transactions to the blockchain with any conflicts being resolved deterministically. Overdraft incorporates incentives for users and is designed to be resilient against Sybil attacks. As a proof of concept, we implemented Overdraft as an Ethereum Solidity smart contract and deployed it on the Sepolia testnet to evaluate its performance.
Blockchain systems, such as Ethereum, are increasingly adopting layer-2 scaling solutions to improve transaction throughput and reduce fees. One popular layer-2 approach is the Optimistic Rollup, which relies on a mechanism known as a dispute game for block proposals. In these systems, validators can challenge blocks that they believe contain errors, and a successful challenge results in the transfer of a portion of the proposer's deposit as a reward. In this paper, we reveal a structural vulnerability in the mechanism: validators may not be awarded a proper profit despite winning a dispute challenge. We develop a formal game-theoretic model of the dispute game and analyze several scenarios, including cases where the proposer controls some validators and cases where a secondary auction mechanism is deployed to induce additional participation. Our analysis demonstrates that under current designs, the competitive pressure from validators may be insufficient to deter malicious behavior. We find that increased validator competition, paradoxically driven by higher rewards or participation, can allow a malicious proposer to significantly lower their net loss by capturing value through mechanisms like auctions. To address this, we propose countermeasures such as an escrowed reward mechanism and a commit-reveal protocol. Our findings provide critical insights into enhancing the economic security of layer-2 scaling solutions in blockchain networks.
Muhammad Rio Kristiawan, Emma Lilianti, Andri Eko Putra
ABSTRACT This study aims to examine the effect of cryptocurrency prices, total cryptocurrencies, number of cryptocurrency transactions on cryptocurrency asset investment decisions. This study is a quantitative study that has the aim of testing data and presenting answers to various problem formulations regarding the effect of cryptocurrency prices, total cryptocurrencies, the number of cryptocurrency transactions on cryptocurrency asset investment decisions. The sample used in this research is 56 time-series data with a period of one last year, obtained by purposive sampling method. The data used is secondary data obtained through the website https://www.coinmarketcap.com and the data is processed using a software called E-views with data analysis techniques using the Ordinary Least Squarer (OLS) and Error Correction Model (ECM) procedures. The results of this study show that the cryptocurrency price variable, total cryptocurrency, the number of cryptocurrency transactions are significantly positive on cryptocurrency asset investment decisions. Keywords: Aset cryptocurrency, Ethereum, Cryptocurrency. ABSTRAK Penelitian ini bertujuan untuk menguji pengaruh harga cryptocurrency, total cryptocurrency, dan jumlah transaksi cryptocurrency terhadap keputusan investasi pada aset crypto. Penelitian ini merupakan studi kuantitatif yang bertujuan untuk menguji data dan memberikan jawaban atas berbagai pertanyaan mengenai dampak harga cryptocurrency, total cryptocurrency, dan jumlah transaksi cryptocurrency terhadap keputusan investasi pada aset crypto. Sampel yang digunakan dalam penelitian ini terdiri dari data, 56 data time series selama satu tahun yang diperoleh dengan metode sampling bertarget. Data yang digunakan adalah data sekunder yang diperoleh dari website https://www.coinmarketcap.com, data dianalisis menggunakan software bernama E-Views dan Ordinary Least Squares (OLS) Teknik analisis data menggunakan metode dan model koreksi kesalahan (ECM) digunakan telah diproses. Hasil penelitian ini menunjukkan bahwa variabel harga cryptocurrency, total cryptocurrency, dan jumlah transaksi cryptocurrency berpengaruh positif signifikan terhadap keputusan investasi pada aset cryptocurrency. Kata kunci: Cryptocurrency Aset, Ethereum, Cryptocurrency.
Smart contracts deployed on blockchain platforms are vulnerable to various security vulnerabilities. However, only a small number of Ethereum contracts have released their source code, so vulnerability detection at the bytecode level is crucial. This paper introduces SmartBugBert, a novel approach that combines BERT-based deep learning with control flow graph (CFG) analysis to detect vulnerabilities directly from bytecode. Our method first decompiles smart contract bytecode into optimized opcode sequences, extracts semantic features using TF-IDF, constructs control flow graphs to capture execution logic, and isolates vulnerable CFG fragments for targeted analysis. By integrating both semantic and structural information through a fine-tuned BERT model and LightGBM classifier, our approach effectively identifies four critical vulnerability types: transaction-ordering, access control, self-destruct, and timestamp dependency vulnerabilities. Experimental evaluation on 6,157 Ethereum smart contracts demonstrates that SmartBugBert achieves 90.62% precision, 91.76% recall, and 91.19% F1-score, significantly outperforming existing detection methods. Ablation studies confirm that the combination of semantic features with CFG information substantially enhances detection performance. Furthermore, our approach maintains efficient detection speed (0.14 seconds per contract), making it practical for large-scale vulnerability assessment.
Dalia Elbanna, Ema Izati Zull Kepili, Nik Hadiyan Nik Azman
Ethereum's anonymity and uncontrolled cryptocurrency attraction have attracted investors.Ethereum's price dynamic inspired this study's prediction analyses.Previous study has focused on either technical analysis or on-chain analysis, leaving investors without the synergistic effects of integrating the two.This study addresses missed insights and lack of cross-comparisons by identifying variable relationships and dependencies and comparing a classical model (ARIMA), a supervised deep learning model (LSTM), and an ensemble machine learning model (XGBoost) in Ethereum price prediction.The dependent variable is Ethereum price and the independent variables are opening, high, low, closing, adjusted closing, volume traded, market capitalization, cumulative return, transactions, blocks, and gas utilized.Prices and market capitalization, traded volume, and volume are strongly correlated, and the LSTM model is the most promising due to its greater prediction accuracy and generality.The analysis reveals the bitcoin market's complexity, affecting investing and risk management.
Kamel Touhami, Ilyes Abidi, Mariem Nsaibi, Maissa Mejri
This study investigates the impact of environmental variables, such as carbon emissions and temperature anomalies, on cryptocurrency returns. While existing research has primarily focused on economic and financial determinants, the influence of environmental factors remains underexplored. Using Dynamic Conditional Correlation GARCH (DCC-GARCH) and Time-Varying Coefficients Vector Autoregression (TVC-VAR) models, this study provides empirical evidence that environmental variables significantly affect the volatility and returns of Bitcoin, Ethereum, and Tether. The results show that Bitcoin and Ethereum are highly sensitive to CO2 emissions and temperature fluctuations, while Tether demonstrates a more moderate response. Moreover, the impact of these environmental factors evolves over time, underscoring their dynamic nature in cryptocurrency valuation. These findings highlight the importance of incorporating environmental variables into forecasting models to enhance risk management and investment strategies. This study contributes to the literature by bridging the gap between environmental concerns and cryptocurrency market behavior, offering valuable insights for investors, regulators, and policymakers.
Kripto paralar 21. yüzyılın ilk çeyreğine damgasını vuran finansal varlıklardır. Finansal piyasalarda işlem görmeye başlamalarının ardından kısa süre içerisinde işlem hacimlerinin artması ile çok sayıda yeni kripto para birimi üretilerek piyasada işlem görmeye başlamıştır. Kripto paraların üretim süreçleri, fiziksel varlığa sahip olmamaları, merkeziyetsiz yapıları gibi geleneksel finansal varlıklardan ayrılan özellikleri dikkat çekmiştir. Dikkat çeken bir diğer önemli özellikleri ise şüphesiz kripto para birimlerinde yaşanan ciddi fiyat dalgalanmaları olmuştur. Kripto para birimlerinin yaşamış oldukları bu fiyat dalgalanmaları piyasanın volatil yapısını ön plana çıkarmıştır. Bu nedenle kripto varlıklar arasındaki volatilite yayılımın analiz edilmesi gerek yatırımcılar gerekse araştırmacılar açısından önem kazanmıştır. Bu çalışmada kripto para piyasasında en yüksek piyasa değerine sahip 4 kripto para birimi arasındaki volatilite yayılımı analiz edilmiştir. Analizlerde BTC (Bitcoin), ETH (Ethereum), BNB (Binance Coin) ve SOL (Solano) için 13.07.2020 ile 05.09.2024 tarihleri arasına ait günlük getiriler kullanılmış ve volatilite yayılımının analizi için TVP-VAR modeli oluşturularak kripto para birimleri arasındaki dinamik bağlantı incelenmiştir. Analiz bulgularından, kripto para birimlerinin volatilitelerindeki toplam dinamik bağlantının Covid-19 Pandemisi ve Bitcoin ETF’lerinin onaylanmasına ilişkin gelişmelerden etkilendiği ve bu dönemlerde artış gösterdiği tespit edilmiştir. Ayrıca, kripto para birimleri arasındaki toplam volatilite yayılımının gücünün yüksek olmadığı, kripto para birimlerinden BNB ve BTC’nin analiz dönemi içerisinde volatilite yayıcısı, ETH ve SOL’un ise volatilite alıcısı özellik gösterdiği bulgusu elde edilmiştir. Kripto para birimleri arasında volatilite yayıcısı olan değişkenler etki güçleri açısından sıralandığında en güçlü volatilite yayıcısı olan para biriminin BNB olduğu ve bunu BTC’nin takip ettiği belirlenmiştir. Diğer yandan SOL, volatilite alıcısı olan kripto para birimleri arasında volatiliteyi en çok alan kripto para birimi olurken, ETH ise ikinci sıradadır. Kripto para birimlerinin volatilitelerindeki değişimin açıklanmasında öncelikle ilgili kripto para biriminin kendi geçmiş fiyat şoklarının etkili olduğu belirlenmiştir. Analizlerde dikkat çeken bir diğer husus ise özellikle BNB ve BTC’nin SOL’a güçlü şekilde volatilite yaymasıdır. Analize dahil edilen 4 kripto para biriminin volatilite yayılım ilişkisinin çok yüksek olmaması, aynı portföyde bulundurulabilecekleri ve birbirlerine risk bulaştırıcı etkilerinin sınırlı olabileceği şeklinde değerlendirilebilir. Bunun yanı sıra BNB’nin en yüksek volatilite yayıcısı olma özelliği dikkate alınarak portföylerin oluşturulması ve takip edilmesi, yatırım verimliliği açısından önem taşıyacaktır. Benzer şekilde SOL’un da diğer kripto para birimlerinden güçlü şekilde volatilite alması, yatırım süreçlerinde dikkat edilmesi gereken bir diğer husus olarak değerlendirilebilir.
Riadh Benammar, Anas Elmelki, Nadia Arfaoui, Adel Boubaker
ABSTRACT This paper investigates how the geopolitical risk (GPRD), economic policy uncertainty (EPU) index, and Twitter economic uncertainty (TEU) related to the Russo‐Ukrainian conflict can affect cryptocurrency returns (Bitcoin [BTC], Ethereum [ETH], Ripple [XRP], Dogecoin [DOGE], Litecoin [LTC], Cardano [ADA], BNB, and TRON [TRX]) over the period ranging from January 1, 2020, to April 24, 2023. Using the Spectral Breitung Candelon causality and wavelet coherence methods, interesting findings are reported. This study reports noteworthy findings. First, we observe that during the armed battle, ADA, BNB, DOGE, LTC, TRX, and XRP appear as hedges against GPRD. However, we found a negative impact on BTC and ETH. Second, the results show that EPU and TEU have no effect on cryptocurrency, respectively. These findings provide a comprehensive overview of cryptocurrency fluctuations during the ongoing conflicts in Ukraine. Finally, findings show that only ADA, BNB, DOGE, LTC, TRX, and XRP could be used as hedging tools during times of uncertainty. These results have practical implications for cryptocurrency investors and elements influencing its returns, especially during uncertain times.
Censorship resistance with short-term inclusion guarantees is an important feature of decentralized systems, missing from many state-of-the-art and even deployed consensus protocols. In leader-based protocols the leader arbitrarily selects the transactions to be included in the new block, and so does a block builder in protocols such as Bitcoin and Ethereum. In a different line of work, since the redundancy of consensus for implementing distributed payments was formally proven, consensusless protocols have been described in theory and deployed in the real world. This has resulted in blockchains and payment systems that are more efficient, and at the same time avoid the centralized role of a leader or block builder. In this report we review existing consensus and consensusless protocols with regard to their censorship-resistance, efficiency, and other properties. Moreover, we present an approach for new constructions with these properties in mind, building on existing leader-based protocols.
M. K. Ghosh, Chirag Dinesh Jain, Raju Halder, Joydeep Chandra
Phishing scams on the Ethereum network have become a serious threat, especially with the influx of new users into the cryptocurrency market. Current detection methods are mainly focused on long-term consistent transaction patterns with smooth temporal dynamics. However, these methods often struggle to differentiate between phishing and non-phishing users, whose behaviours may appear deceptively similar. Additionally, they face challenges such as network sparsity and data leakage, leading to significant performance limitations. To address these issues, we introduce TEMPER, a novel sequential learning framework designed to jointly capture the subtle distinctions between long- and short-term user behaviours and their correlations to provide more comprehensive insights. TEMPER effectively generates distinguishable user embeddings, enabling the accurate identification of phishing users. Unlike previous approaches, TEMPER mitigates data leakage through a novel sequential transaction sampling algorithm and addresses network sparsity with short-term temporal learning. Through extensive experimentation on three real-world Ethereum datasets, TEMPER demonstrates its efficacy by achieving a 3-4% improvement in the F1-Score compared to existing baseline models, representing a significant advancement in Ethereum phishing user detection.
This article is an ethnographic account of the State Machine One (SMO) gigalopolis. It offers the first modern mapping of the social imaginaries found within the network states, coordi-nations and agoras of State Machine One (SMO). It further provides insight into the operations of SMO’s Ethereum Ecosystem C (EEC) polycentric governance system. The author was invited to engage in this research by the University of Dencun and received academic immunity to travel across the major sectors of SMO. Employing New Grounded Theory, it presents State Machine One as a complex, heterogenous, but broadly cohesive political entity at its core. The author finds strong evidence of Ethereum Alignment across SMO, but also the presence of an ungovernable region known as Free Ross and some unusual reports from beyond the Exclusion Border.
Chun-Wing Poon, James T. Kwok, Calvin Chow, Jieun Choi
Anti-money laundering (AML) systems are important for protecting the global economy. However, conventional rule-based methods rely on domain knowledge, leading to suboptimal accuracy and a lack of scalability. Graph neural networks (GNNs) for digraphs (directed graphs) can be applied to transaction graphs and capture suspicious transactions or accounts. However, most spectral GNNs do not naturally support multi-dimensional edge features, lack interpretability due to edge modifications, and have limited scalability owing to their spectral nature. Conversely, most spatial methods may not capture the money flow well. Therefore, in this work, we propose LineMVGNN (Line-Graph-Assisted Multi-View Graph Neural Network), a novel spatial method that considers payment and receipt transactions. Specifically, the LineMVGNN model extends a lightweight MVGNN module, which performs two-way message passing between nodes in a transaction graph. Additionally, LineMVGNN incorporates a line graph view of the original transaction graph to enhance the propagation of transaction information. We conduct experiments on two real-world account-based transaction datasets: the Ethereum phishing transaction network dataset and a financial payment transaction dataset from one of our industry partners. The results show that our proposed method outperforms state-of-the-art methods, reflecting the effectiveness of money laundering detection with line-graph-assisted multi-view graph learning. We also discuss scalability, adversarial robustness, and regulatory considerations of our proposed method.
Ilham Qasse, Isra M. Ali, Nafisa Ahmed, Mohammad Hamdaqa · 5 authors
The immutability of smart contracts on blockchain platforms like Ethereum promotes security and trustworthiness but presents challenges for updates, bug fixes, or adding new features post-deployment. These limitations can lead to vulnerabilities and outdated functionality, impeding the evolution and maintenance of decentralized applications. Despite various upgrade mechanisms proposed in academic research and industry, a comprehensive analysis of their trade-offs and practical implications is lacking. This study aims to systematically identify, classify, and evaluate existing smart contract upgrade mechanisms, bridging the gap between theoretical concepts and practical implementations. It introduces standardized terminology and evaluates the trade-offs of different approaches using software quality attributes. We conducted a Multivocal Literature Review (MLR) to analyze upgrade mechanisms from both academic research and industry practice. We first establish a unified definition of smart contract upgradeability and identify core components essential for understanding the upgrade process. Based on this definition, we classify existing methods into full upgrade and partial upgrade approaches, introducing standardized terminology to harmonize the diverse terms used in the literature. We then characterize each approach and assess its benefits and limitations using software quality attributes such as complexity, flexibility, security, and usability. The analysis highlights significant trade-offs among upgrade mechanisms, providing valuable insights into the benefits and limitations of each approach. These findings guide developers and researchers in selecting mechanisms tailored to specific project requirements.
Cheick Tidiane Bâ, Benjamin A. Steer, Matteo Zignani, Richard G. Clegg
Blockchain technology and cryptocurrencies have garnered considerable attention over the past 15 years. The term Web3 (sometimes Web 3.0) has been coined to define a possible direction for the web based on the use of decentralisation via blockchain. Cryptocurrencies are characterised by high market volatility and susceptibility to substantial crashes, issues that require temporal analysis methodologies able to tackle the high temporal resolution, heterogeneity, and scale of blockchain data. While existing research attempts to analyse crash events, fundamental questions persist regarding the optimal timescale for analysis, differentiation between long-term and short-term trends, and the identification and characterisation of shock events within these decentralised systems. This article addresses these issues by examining cryptocurrencies traded on the Ethereum blockchain, with a spotlight on the crash of the stablecoin TerraUSD (UST) and the currency LUNA designed to stabilise it. Utilising complex network analysis and a multi-layer temporal graph allows the study of the correlations between the layers representing the currencies and system evolution across diverse timescales. The investigation sheds light on the strong interconnections among stablecoins pre-crash and the significant post-crash transformations. We identify anomalous signals before, during, and after the collapse, emphasising their impact on graph structure metrics and user movement across layers. This article is novel in its use of temporal, cross-chain graph analysis to explore a cryptocurrency collapse. It emphasises the importance of temporal analysis for studies on web-derived data. In addition, the methodology shows how graph-based analysis can enhance traditional econometric results. Overall, this research carries implications beyond its field, for example, for regulatory agencies aiming to safeguard users could use multi-layer temporal graphs as part of their suite of analysis tools.
M. M. Khan, Fahd Sikandar Khan, Muhammad Nadeem, Taimur Khan · 6 authors
Blockchain technology has emerged as a transformative solution for secure, immutable, and decentralized data management across diverse domains, including economics, healthcare, and supply chain management. Given its soaring adoption, it is crucial to assess the suitability of various blockchain platforms for specific applications. This study evaluates the performance of Hyperledger Fabric (HF) and private Ethereum (Geth) to analyze their scalability (node count), throughput (transactions per second (TPS)), and latency (measured in milliseconds). A benchmarking tool was developed in-house to assess the execution of key smart contract functions—QueryUser, CreateUser, TransferMoney, and IssueMoney—under varying transaction loads (10–1000 transactions) and network sizes (2–16 node count). The results indicate that HF performs significantly better than private Ethereum in terms of invoke functions, achieving up to 5× throughput and up to 26× lower latency. However, private Ethereum excels in query operations because of its account-based ledger model. While Hyperledger Fabric scales efficiently within moderate transaction volumes, it experiences concurrency limitations beyond 1000 transactions, whereas private Ethereum processes up to 10,000 transactions, albeit with performance fluctuations due to gas fees. The findings offer valuable insights into the strengths and tradeoffs of both platforms, informing optimal blockchain selection for enterprise applications that require high transaction efficiency.
As the number of decentralized applications and users on Ethereum grows, the ability of the blockchain to efficiently handle a growing number of transactions becomes increasingly strained. Ethereums current execution model relies heavily on sequential processing, meaning that operations are processed one after the other, which creates significant bottlenecks to future scalability demands. While scalability solutions for Ethereum exist, they inherit the limitations of the EVM, restricting the extent to which they can scale. This paper proposes a novel solution to enable maximally parallelizable executions within Ethereum, built out of three self-sufficient approaches. These approaches include strategies in which Ethereum transaction state accesses could be strategically and efficiently predetermined, and further propose how the incorporation of gas based incentivization mechanisms could enforce a maximally parallelizable network.
Prof. Pritesh Patil, Pranav Dhote, S.S. Kulkarni, Ketan Agrawal
Modern interconnected society creates ongoing challenges to charitable giving because donors need greater assurance of transparency and financial accountability. A new Ethereum-based solution from our research removes intermediaries by establishing an application dedicated to charitable activities. The DApp provides an integrated system for traditional offers and conditional funding structures which operates on blockchain technologies at base level. A framework of Solidity smart contracts connects with React.js frontend components and Ethers.js implements the blockchain communication protocols to deliver a smooth donor transaction process. The platform features milestone-based withdrawals that functions to distribute crowdfunded money after specific campaign targets have been reached thus building transparent reporting. The system gives contributors complete control between funding registered organizations directly and specific projects where each financial transaction is recorded permanently on the blockchain ledger. The unbending nature of blockchain as a record system provides historic visibility for all charitable transactions. Through distributed ledger technology implementation our framework provides donors both simple donation processes and a modern model for reliable philanthropic activities which allow full monitoring of every charitable contribution.
One of the most emblematic theorems in the theory of distributed databases is Eric Brewer’s CAP theorem. It stresses the tradeoffs between Consistency, Availability, and Partition and states that it is impossible to guarantee all three of them simultaneously. Inspired by this, we introduce the new CAP theorem for autonomous consensus systems, and we demonstrate that, at most, two of the three elementary properties, Consensus achievement (C), Autonomy (A), and entropic Performance (P) can be optimized simultaneously in the generic case. This provides a theoretical limit to Blockchain systems’ decentralization, impacting their scalability, security, and real-world adoption. To formalize and analyze this tradeoff, we utilize the IoT micro-Blockchain as a universal, minimal, consensus-enabling framework. We define a set of quantitative functions relating each of the properties to the number of event witnesses in the system. We identify the existing mutual exclusions, and formally prove for one homogenous system consideration, that (A), (C), and (P) cannot be optimized simultaneously. This suggests that a requirement for concurrent optimization of the three properties cannot be satisfied in the generic case and reveals an intrinsic limitation on the design and the optimization of distributed Blockchain consensus mechanisms. Our findings are formally proved utilizing the IoT micro-Blockchain framework and validated through the empirical data benchmarking of large-scale Blockchain systems, i.e., Bitcoin, Ethereum, and Hyperledger Fabric.
Eleni Koutrouli, Polychronis Manousopoulos, John Theal, Laura Tresso
As crypto assets become more widely adopted, crypto asset markets and traditional financial markets may become increasingly interconnected. The close linkages between these markets have potentially important implications for price formation, contagion, risk management and regulatory frameworks. In this study, we assess the correlation between traditional financial markets and selected crypto assets, study factors that may impact the price of crypto assets and identify potentially significant events that may have an impact on Bitcoin and Ethereum price dynamics. For the latter analyses, we adopt a Bayesian model averaging approach to identify change points in the Bitcoin and Ethereum daily price time series. We then use the dates and probabilities of these change points to link them to specific events, finding that nearly all of the change points can be associated with known historical crypto asset-related events. The events can be classified into broader geopolitical developments, regulatory announcements and idiosyncratic events specific to either Bitcoin or Ethereum.
This study investigates return spillovers among the 15 most capitalized cryptocurrencies during the Russia-Ukraine war and the COVID-19 pandemic. Data were extracted from the Coin Market Cap database to ensure a comprehensive analysis of market behavior, covering a daily series from January 2020 to December 2023. The research employs three autoregressive techniques (TVP-VAR, LASSO VAR, and Ridge VAR) to verify the robustness of findings regarding market fragility influenced by non-economic shocks. The study identifies extensive return spillovers primarily driven by Bitcoin and Ethereum, with considerable influences from Cardano, Litecoin, and Polkadot. The results show Ethereum as a primary spillover transmitter in the cryptocurrency market, taking that position formerly held by Bitcoin. Despite the speculative nature of cryptocurrencies, there is potential for diversification through two stablecoins, Tether and USD Coin, which exhibit limited spillover effects from other cryptocurrencies and negative correlations with one another. As a stablecoin, DAI served as a potential diversifier during the COVID-19 pandemic but not during the Ukraine war. The study offers practical insights for investors on managing crypto portfolios during geopolitical and global health crises and the strategic use of stablecoins. Societally, the study examines the need for enhanced regulatory frameworks to reduce systemic risks in the highly interconnected cryptocurrency market. JEL Classification: G01, G11.