Tao Yan, Shengnan Li, Benjamin Kraner, Luyao Zhang · 5 authors
Ethereum 2.0, as the preeminent smart contract blockchain platform, guarantees the precise execution of applications without third-party intervention. At its core, this system leverages the Proof-of-Stake (PoS) consensus mechanism, which utilizes a stochastic process to select validators for block proposal and validation, consequently rewarding them for their contributions. However, the implementation of blockchain technology often diverges from its central tenet of decentralized consensus, presenting significant analytical challenges. Our study collects consensus reward data from the Ethereum Beacon chain and conducts a comprehensive analysis of reward distribution and evolution, categorizing them into attestation, proposer and sync committee rewards. To evaluate the degree of decentralization in PoS Ethereum, we apply several inequality indices, including the Shannon entropy, the Gini Index, the Nakamoto Coefficient, and the Herfindahl-Hirschman Index (HHI). Our comprehensive dataset is publicly available on Harvard Dataverse, and our analytical methodologies are accessible via GitHub, promoting open-access research. Additionally, we provide insights on utilizing our data for future investigations focused on assessing, augmenting, and refining the decentralization, security, and efficiency of blockchain systems.
Tao Yan, Shengnan Li, Benjamin Kraner, Luyao Zhang · 5 authors
Ethereum 2.0, as the preeminent smart contract blockchain platform, guarantees the precise execution of applications without third-party intervention. At its core, this system leverages the Proof-of-Stake (PoS) consensus mechanism, which utilizes a stochastic process to select validators for block proposal and validation, consequently rewarding them for their contributions. However, the implementation of blockchain technology often diverges from its central tenet of decentralized consensus, presenting significant analytical challenges. Our study collects consensus reward data from the Ethereum Beacon chain and conducts a comprehensive analysis of reward distribution and evolution, categorizing them into attestation, proposer, and sync committee rewards. To evaluate the degree of decentralization in PoS Ethereum, we apply several inequality indices, including the Shannon entropy, the Gini Index, the Nakamoto Coefficient, and the Herfindahl-Hirschman Index (HHI). Our comprehensive dataset is publicly available on Harvard Dataverse, and our analytical methodologies are accessible via GitHub, promoting open-access research. Additionally, we provide insights on utilizing our data for future investigations focused on assessing, augmenting, and refining the decentralization, security, and efficiency of blockchain systems.
Decentralized autonomous organizations (DAOs) are not a novel social phenomenon; rather, they draw inspiration from self-organizing systems and are often regarded as digital counterparts of cooperatives (Co-ops), wherein members fully own and govern the organization. The advancement of digital solutions for decentralization, such as Distributed Ledger Technology (DLT), along with the emergence of the third generation of websites (Web3) and platforms, has propelled DAOs to a new echelon. As such, DAOs represent the next generation of organizations, aptly referred to as Organization 5.0 in the context of Society 5.0. The objective of this paper is to provide a comprehensive overview of the evolutionary trajectory of decentralized autonomous organizations and their classification. The advent of Ethereum in 2015 enabled the realization of DAOs, with "The DAO" being the first large-scale example established in 2016 as a decentralized venture fund within the Ethereum ecosystem. Over time, DAOs have expanded their scope beyond fundraising and have evolved to serve various purposes. To provide a comprehensive context, the paper presents background information on the evolution of blockchain applications and discusses ethical considerations related to DAOs. In order to identify the most common categories of DAOs, this paper consults various DAO explorers and include, for each identified category, a descriptive example of a DAO. Finally, the paper concludes by offering an outlook on the future of DAOs.
Since 2009 when the first cryptocurrency Bitcoin began to be inserted into the market of electronic currencies, today in 2023 there are more than 19,850 electronic cryptocurrencies [1]. According to information from the coinecko website, the cryptocurrency market has expanded dramatically from a market capitalization of $1 million in 2013 to $3 trillion in November 2021 [2]. Referring to the latest statistical data, 3 are the cryptocurrencies that rule the e-commerce market in November 2023, Bitcoin,Ethereum AND Tether USDt [3]. Robotic Process Automation (RPA) is a growing trend in the restructuring of business processes, combined with digital transformation. This technology can be applied in different areas of business processes and by organizations from any activity sector [4].With continuous advances in automated processes through RPA, mechanisms involving Artificial Intelligence (AI) were incorporated to influence real-life decision-making [5]. Artificial Intelligence (AI) allows improving the accuracy and execution of RPA processes in extracting information and recognizing, classifying, predicting and optimizing processes [6].Nowadays, artificial intelligence is affecting the way people process computer data, televisions have started to create avatars that they use for news reporting. In this paper we will study the impact that the use of automatic sale and purchase of electronic cryptocurrencies can have using IPA and RPA and the possibility of this process being realized through this process.
Formal verification of smart contracts has become a hot topic in academic and industrial research, given the growing value of assets managed by decentralized applications and the consequent incentive for adversaries to tamper with them. Most of the current research on the verification of contracts revolves around Solidity, the main high-level language supported by Ethereum and other leading blockchains. Although bug detection tools for Solidity have been proliferating almost since the inception of Ethereum, only in the last few years we have seen verification tools capable of proving that a contract respects some desirable properties. An open issue is how to evaluate and compare the effectiveness of these tools: indeed, the existing benchmarks for general-purpose programming languages cannot be adapted to Solidity, given substantial differences in the programming model and in the desirable properties. We address this problem by proposing an open benchmark for Solidity verification tools. By exploiting our benchmark, we compare two leading tools, SolCMC and Certora, discussing their completeness, soundness and expressiveness limitations.
Blockchain market value peaked at $3 trillion, fell to $1 trillion, then recovered to $1.5 trillion and is rising again. Blockchain accounts secure most on-chain assets in this huge market (Web-12). This paper initiates a universal blockchain account model from a comprehensive review of blockchain account development, encompassing both academic and industry perspectives. This paper uses a model analysis method to analysis the account progress and create high level new account model. And it uses systematic literature review method to search, filter, analysis and evaluate the papers about account models and analyzes related technology trade-offs. Searching with key words: blockchain, account, private key and security in WOS, Scopus and Bitcoin and Ethereum community repositories, this research provides in-depth insights into the design and evaluation of account models, from traditional bank accounts to Bitcoin, EVM-adaptable, and abstraction accounts. Through data-driven comparisons of account models (security, cost, adoption), this study also explores future directions and provides an overview of cross-model account theory, guiding further blockchain research. This paper leaves deeper dives into model change drivers, application technology advancements.
We introduce a model that derives a metric to answer the question: what is the expected gain of a staker? We calculate the rewards as the staking return in a Proof-of-Stake (PoS) consensus context. For each period of block validation and by a forward approach, we prove that the interest is given by the ratio of the average staking gain to the total staked coins. Some additional PoS features are considered in the model, such as slash rate and Maximal Extractable Value (MEV), which marks the originality of this approach. In particular, we prove that slashing diminishes the rewards, reflecting the fact that the blockchain can consider stakers to potentially validate incorrectly. Regarding MEV, the approach we have sheds light on the relation between transaction fees and the average staking gain. We illustrate the developed model with Ethereum 2.0 and apply a similar process in a Proof-of-Work consensus context.
Mikel Cortes-Goicoechea, Csaba Király, Dmitriy Ryajov, José L. Muñoz · 5 authors
Scalability in blockchain remains a significant challenge, especially when prioritizing decentralization and security. The Ethereum community has proposed comprehensive data-sharding techniques to overcome storage, computational, and network processing limitations. In this context, the propagation and availability of large blocks become the subject of research to achieve scalable data-sharding. This paper provides insights after exploring the usage of a Kademlia-based Distributed Hash Table (DHT) to enable Data Availability Sampling (DAS) in Ethereum. It presents a DAS-DHT simulator to study this problem and validates the results of the simulator with experiments in a real DHT network, InterPlanetary File System (IPFS). Our results help us understand what parts of DAS can be achieved based on existing Kademlia DHT solutions and which ones cannot. We discuss the limitations of DHT solutions and discuss other alternatives.
Users bid in a transaction fee mechanism (TFM) to get their transactions included and confirmed by a blockchain protocol. Roughgarden (EC'21) initiated the formal treatment of TFMs and proposed three requirements: user incentive compatibility (UIC), miner incentive compatibility (MIC), and a form of collusion-resilience called OCA-proofness. Ethereum's EIP-1559 mechanism satisfies all three properties simultaneously when there is no contention between transactions, but loses the UIC property when there are too many eligible transactions to fit in a single block. Chung and Shi (SODA'23) considered an alternative notion of collusion-resilience, called $c$-side-contract-proofness ($c$-SCP), and showed that, when there is contention between transactions, no TFM can satisfy UIC, MIC, and $c$-SCP for any $c\geq 1$. OCA-proofness asserts that the users and a miner should not be able to "steal from the protocol." On the other hand, the $c$-SCP condition requires that a coalition of a miner and a subset of users should not be able to profit through strategic deviations (whether at the expense of the protocol or of the users outside the coalition). Our main result is the first proof that, when there is contention between transactions, no (possibly randomized) TFM in which users are expected to bid truthfully satisfies UIC, MIC, and OCA-proofness.This result resolves the main open question in Roughgarden (EC'21). We also suggest several relaxations of the basic model that allow our impossibility result to be circumvented.
Naomi A. Arnold, Peijie Zhong, Cheick Tidiane Bâ, Benjamin A. Steer · 8 authors
Distributed ledger technologies have opened up a wealth of fine-grained transaction data from cryptocurrencies like Bitcoin and Ethereum. This allows research into problems like anomaly detection, anti-money laundering, pattern mining and activity clustering (where data from traditional currencies is rarely available). The formalism of temporal networks offers a natural way of representing this data and offers access to a wealth of metrics and models. However, the large scale of the data presents a challenge using standard graph analysis techniques. We use temporal motifs to analyse two Bitcoin datasets and one NFT dataset, using sequences of three transactions and up to three users. We show that the commonly used technique of simply counting temporal motifs over all users and all time can give misleading conclusions. Here we also study the motifs contributed by each user and discover that the motif distribution is heavy-tailed and that the key players have diverse motif signatures. We study the motifs that occur in different time periods and find events and anomalous activity that cannot be seen just by a count on the whole dataset. Studying motif completion time reveals dynamics driven by human behaviour as well as algorithmic behaviour.
Iulian Aciobăniţei, Ştefan-Ciprian Arseni, Emil Bureacă, Mihai Togan
The current shift towards digital transactions emphasizes the need for robust Qualified Electronic Signature (QES) frameworks that safeguard integrity and privacy. Having the potential to become the leading type of adopted QES, the main challenge that Remote QESs present to end users is choosing between transmitting the entire document or only its digest to the Trust Service Provider (TSP). The first option compromises the document’s confidentiality, while the second one requires the development of signature applications compliant with advanced signature formats, a task that often needs additional time and resources. In this paper, we introduce a comprehensive strategy for remote QESs, designed for seamless integration with current client applications, while simultaneously maintaining user privacy. The main topics approached in this paper are the following: a comprehensive architecture for privacy-aware remote QES systems, relevant standards and legislation, integration scenarios for clients, and remote QES standard protocols to assure communication between client and TSP environments. Furthermore, we also explore the integration of our proposed solution with an enhanced long-term preservation service that uses Ethereum smart contracts and methodologies to implement signature applications with advanced electronic signatures via open-source libraries while ensuring document privacy. The main result of this work is a flexible on-premise module that provides the ability to sign, validate, and preserve documents, with a minimal integration effort.
William Lorenzoni Zottele, Fabrício Tonetto Londero, Sylvio André Garcia Vieira, Ana Paula Canal
A tecnologia blockchain e seus contratos inteligentes estão revolucionando os negócios online ao conectar usuários sem intermediários, aumentando a confiança. Os usuários podem armazenar dados em uma rede global imutável e criar os chamados Smart Contracts. O objetivo deste trabalho é criar um contrato inteligente de arrecadação de fundos coletivos usando a tecnologia blockchain. Neste, os colaboradores têm controle dos fundos arrecadados por meio de votações em pedidos de saques propostos pelo fundador do contrato. O contrato foi desenvolvido na linguagem de programação Solidity utilizando a plataforma Remix.
Indri Tri Julianto, Dede Kurniadi, Ricky Rohmanto, Fathia Alisha Fauzia
Ethereum is a cryptocurrency that is now the second most popular digital asset after Bitcoin. High trading volume is the trigger for the popularity of this cryptocurrency. In addition, Ethereum is home to various decentralized applications and acts as a link for Decentralized Finance (DeFi) transactions, Non-Fungible Tokens (NFTs) and the use of smart contracts in the crypto space. This study aims to improve the performance of the forecasting algorithm by using feature extraction for Ethereum price forecasting. The algorithms used are neural networks, deep learning, and support vector machines. The research methodology used is Knowledge Discovery in Databases. The data set used comes from the yahoo.finance.com website regarding Ethereum prices. The results show that the neural network Algorithm is the best Algorithm compared to Deep Learning and support vector machine. The root mean square error value for the neural network before feature selection is 93,248 +/- 168,135 (micro average: 186,580 +/- 0,000) Linear Sampling method and 54,451 +/- 26,771 (micro average: 60,318 +/- 0,000) Shuffled Sampling method. Then after feature selection, the root mean square error value improved to 38,102 +/- 31,093 (micro average: 48,600 +/- 0,000) using the Shuffled Sampling method
Unknown vulnerabilities, also known as zero-day vulnerabilities, are vulnerabilities in software, systems, or networks that have not yet been publicly disclosed or fixed. If these vulnerabilities are ever discovered by hackers, intentionally or unintentionally, they pose a major threat to network security. This is particularly true in the blockchain field, as smart contracts hold a lot of money, and if they are discovered and exploited by hackers, the financial losses to users will be even greater. However, the current research on smart contract vulnerabilities mainly focuses on known vulnerabilities, and the research on unknown vulnerabilities has been limited. Based on this, we introduce a machine learning-based method for detecting unknown vulnerabilities in smart contracts. First, the method obtains the opcode sequences executed by smart contract transactions in the EVM by instrumenting Geth and replaying the Ethereum transactions. Next, we employ an n-gram model and a vector weight penalty mechanism to extract the opcode sequence features. We then use machine learning algorithms to detect unknown vulnerabilities based on the similarity principle. Finally, we test the effectiveness of our method with four machine learning models: the K-Nearest Neighbor algorithm (KNN), Support Vector Machine (SVM), Logistic Regression (LR), and Decision Tree (DT). The SVM model performs best at detecting unknown vulnerabilities, with an accuracy of 96%, a precision of 91%, a recall of 100%, and an F1-score of 95%. We also discuss the benefits of the method: timely detection of attacks due to unknown vulnerabilities, thus reducing user losses.
Guillermo Angeris, Theo Diamandis, Ciamac Moallemi
In this paper we show that, using only mild assumptions, previously proposed multidimensional blockchain fee markets are essentially optimal, even against worst-case adversaries. In particular, we show that the average welfare gap between the following two scenarios is at most $O(1/\sqrt{T})$, where $T$ is the length of the time horizon considered. In the first scenario, the designer knows all future actions by users and is allowed to fix the optimal prices of resources ahead of time, based on the designer's oracular knowledge of those actions. In the second, the prices are updated by a very simple algorithm that does not have this oracular knowledge, a special case of which is similar to EIP-1559, the base fee mechanism used by the Ethereum blockchain. Roughly speaking, this means that, on average, over a reasonable timescale, there is no difference in welfare between 'correctly' fixing the prices, with oracular knowledge of the future, when compared to the proposed algorithm. We show a matching lower bound of $Ω(1/\sqrt{T})$ for any implementable algorithm and also separately consider the case where the adversary is known to be stochastic.
Non-Fungible Tokens (NFTs) have arisen as a major term in the twenty-first century, signifying unique digital assets such as photographs, music, and movies. These tokens receive their value and legitimacy from blockchain technology, which is mostly used on the Ethereum platform. NFTs, which use smart contracts, enable the production, ownership, and trading of one-of-a-kind digital assets, revolutionizing sectors including as art, music, and gaming. While blockchain’s uses are broad, there are worries about the environmental effect of blockchain, notably Ethereum’s energy-intensive proof-of-work consensus. Despite their revolutionary potential, NFTs pose dangers connected to market instability, legal concerns, and technological weaknesses. NFT trading is enabled by a variety of third-party programmers and markets, with notable participants being OpenSea and MetaMask. Looking ahead, the future of NFTs in India is dependent on legislative changes, as the country sees an increase in interest in these digital assets among artists and producers, necessitating a balanced approach to encourage innovation while resolving possible difficulties. NFTs are a game changer in the digital economy, opening up new opportunities for producers and investors. However, environmental problems and regulatory uncertainties must be resolved in order for the NFT ecosystem to expand sustainably. Technological improvements, legislative reforms, and the continuous growth of blockchain technology will most likely influence the future of NFTs in India and throughout the world.
A smart contract is a computerised transaction agreement that carries out predefined terms without human involvement or third-party intermediaries. It serves as a trust intermediary in several industries, including finance, insurance, and supply chain management, in the blockchain 2.0 era. With the increasing interest in smart contracts, security has become a serious problem. Examining typical vulnerability types and vulnerability detection methodologies is of special importance. In this research, a comprehensive evaluation of common smart contract security vulnerabilities is conducted, and a three-tier threat model is then provided to classify the vulnerabilities. In addition, we examine fourteen existing smart contract analysis tools for finding vulnerabilities and classify them according to the main technique they apply. This article is designed to serve as a reference for people who wish to analyse deployed code and enhance existing detection techniques. At the conclusion, open issues and future research paths regarding smart contract vulnerability detection are presented.
Open access
2 source records
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
Shimal Sh. Taher, Siddeeq Y. Ameen, Jihan A. Ahmed
In recent years, cryptocurrencies have experienced rapid growth and adoption, revolutionizing the financial sector. However, the rise of digital currencies has also led to an increase in fraudulent transactions and illegal activities. In this paper, we present a comprehensive study on the detection of fraudulent transactions in the context of cryptocurrency exchanges, with a primary focus on the Ethereum network. By employing various Machine Learning (ML) techniques and ensemble methods, including the hard voting ensemble model, which achieved a remarkable 99% accuracy, we aim to effectively identify suspicious transactions while maintaining high accuracy and precision. Additionally, we delve into the importance of eXplainable Artificial Intelligence (XAI) to enhance transparency, trust, and accountability in AI-based fraud detection systems. Our research contributes to the development of reliable and interpretable models that can significantly improve the cryptocurrency ecosystem security and integrity.
Blockchain and cryptocurrencies have emerged as revolutionary technologies that have garnered substantial interest in the past few years. A blockchain functions as a distributed and transparent ledger, facilitating secure transactions without the requirement of intermediaries. Digital assets like Bitcoin and Ethereum are examples of cryptocurrencies, leveraging blockchain technology to enable secure and unrestricted transactions. These innovations have the potential to revolutionize industries such as finance, supply chain management, and healthcare. However, challenges such as regulatory concerns and scalability issues must be addressed for their widespread adoption. Overall, blockchain and cryptocurrencies offer new opportunities for innovation and financial inclusion
Mindaugas Juodis, Ernestas Filatovas, Remigijus Paulavičius
The decentralization paradigm has made blockchain one of the most disruptive technologies today. When evaluating the level of decentralization, the key metric for most public blockchain networks is the degree of decentralization of the resources responsible for determining who generates the blocks. In turn, it facilitates a greater understanding of both security and scalability on a blockchain. This work provides an overview of the current state-of-the-art on wealth decentralization, which has not yet received the attention it deserves. We collect data, calculate various wealth decentralization metrics, and compare our results with research on the same methodology. As the amount of data for various blockchains increases rapidly, it is helpful to have techniques to aggregate data for statistical analysis. We introduce and provide conservative estimates of decentralized group metrics based on the reduced data and compare them with full-data measurements. Our research considers both the Layer 1 blockchains of Bitcoin and Ethereum, along with Layer 2 blockchains such as Arbitrum, Optimism, and Polygon.
This work intends to comprehensively analyze the application of blockchain technology in enterprise financial accounting information sharing and address prevalent issues such as information opacity, data tampering, and data security in the current practices. Therefore, it writes smart contracts based on the Ethereum platform to achieve the secure sharing of financial accounting information between enterprises. This work employs a randomized experimental design approach, using a computer-generated random number program to divide 100 enterprises into experimental and control groups, each comprising 50 enterprises. Enterprises in the experimental group share financial accounting information using smart contracts on the Ethereum platform during the experiment. The financial personnel of these enterprises upload reconciled data to the corresponding smart contracts using the enterprise's digital signatures after each month's accounting process. Enterprises in the control group continue to use traditional methods of financial accounting information sharing (such as email and web platforms) to share financial data files directly. Quantitative analysis is performed to compare the data between the experimental and control groups. Empirical results reveal a notable enhancement in information-sharing efficiency by 25.7%, a 19.8% improvement in data accuracy, and a 13.6% reduction in financial information-sharing costs within the experimental group compared to the control group. This work provides compelling evidence that adopting blockchain-based information-sharing methods can effectively elevate data trustworthiness and security. Supported by systematic empirical findings, this work validates the significant potential of blockchain technology in the realm of enterprise financial accounting information sharing.
Alexandr Kuznetsov, Alex Rusnak, Anton Yezhov, Kateryna Kuznetsova · 6 authors
In the rapidly evolving landscape of blockchain technology, ensuring the integrity and security of data is paramount. This study delves into the security aspects of Merkle Trees, a fundamental component in blockchain architectures, such as Ethereum. We critically examine the susceptibility of Merkle Trees to hash collisions, a potential vulnerability that poses significant risks to data security within blockchain systems. Despite their widespread application, the collision resistance of Merkle Trees and their robustness against preimage attacks have not been thoroughly investigated, leading to a notable gap in the comprehensive understanding of blockchain security mechanisms. Our research endeavors to bridge this gap through a meticulous blend of theoretical analysis and empirical validation. We scrutinize the probability of root collisions in Merkle Trees, considering various factors such as hash length and path length within the tree. Our findings reveal a direct correlation between the increase in path length and the heightened probability of root collisions, thereby underscoring potential security vulnerabilities. Conversely, we observe that an increase in hash length significantly reduces the likelihood of collisions, highlighting its critical role in fortifying security. The insights garnered from our research offer valuable guidance for blockchain developers and researchers, aiming to bolster the security and operational efficacy of blockchain-based systems.