Aman Shekhar, Sagar Vashnav, Rishav Jha, Pallavi Pandey
Refugees seeking asylum in India often face numerous challenges, including a lack of legal documentation, limited access to essential services, and the risk of exploitation. In this paper, we propose a decentralized system that leverages smart contract technology to create digital identities for refugees, enabling them to access essential services such as employment and housing. Our system stores all their data, including biometrics, in a secure and decentralized manner on the Ethereum blockchain. Based on this data, we create a working permit and a temporary identification card in the form of non-fungible tokens (NFTs) that can be used to access essential services. Additionally, we create a crypto wallet linked to their digital identity, enabling them to perform regular transactions like purchasing rations, paying for their housing and electricity bills and other necessities. Furthermore, we would design a smart contract that tracks the refugees’ monthly income and provides government financial aid directly to their crypto wallet. The contract also automatically adjusts the amount of financial aid based on the refugee’s income, ensuring an even and non-corrupted distribution of government funds. Through our system, we aim to empower refugees with financial stability, enabling them to lead healthy and productive lives while contributing to India’s economy. Our solution is transparent, secure, and easily accessible, making it a viable option for addressing the challenges faced by refugees in India.
Ethereum introduced decentralized applications (DApps) to leverage blockchain's potential, distinct from the cryptocurrency buzz dominating discussions. Ethereum offers a robust platform for DApp development, utilizing the Ethereum Virtual Machine (EVM). With increasing smart contract integration, Ethereum's ecosystem strengthens, as these self-executing contracts govern transactions while ensuring consensus among all participants. In democratic societies, the demand for a transparent and secure electronic voting platform is high. Ethereum, with its expansive network, is an ideal choice for such projects. Our implementation focuses on a decentralized, blockchain-based voting system. Multiple nodes collectively store voting data, ensuring redundancy. In case of node issues, users can access their data from operational nodes, enhancing system reliability. This contrasts with traditional centralized servers, prone to hacking or failures that compromise data accessibility and integrity. Blockchain's immutable data storage prevents tampering, as nodes independently verify each block using cryptographic hash codes. Unauthorized changes trigger immediate alerts, enhancing security and trust in the voting system.
The growing global fascination with cryptocurrencies has sparked heightened interest, driven by their pronounced market volatility. This particular study endeavors to assess the risk and rewards associated with four prominent cryptocurrencies, while also delving into an examination of their interrelationships and fluctuation patterns. The investigation is based on daily closing prices spanning from January 1, 2017, to June 30, 2022. To unravel the spillover and asymmetrical repercussions of volatility, we employ various models from the GARCH family, most notably the DCC GARCH and EGARCH models. In addition, Granger causality is harnessed to uncover any causal connections among these digital assets. The findings underscore a noteworthy spillover phenomenon between Bitcoin and Ethereum, the two foremost cryptocurrencies boasting the maximum market capitalization. This spillover effect manifests as symmetric volatility impacts, setting them apart from Litecoin and RIPPLE.
Purpose:This article delves into the transformative potential of smart contracts in revolutionizing procurement within supply chain management, with a special focus on the healthcare industry. In line with the challenges posed by complex procurement processes, the study explores how self-executing digital contracts can enhance efficiency, security, and transparency. Theoretical framework:The article is grounded in the concept of smart contracts and their applications in procurement, serving as a bridge between emerging technology and supply chain management innovation. It draws upon an extensive body of literature and research related to smart contracts, supply chain management, and technology adoption. The study is designed to build upon this theoretical foundation, using two comprehensive case studies to provide real-world insights and practical applications. These case studies, situated within the healthcare industry, serve as tangible examples of how the theoretical concepts surrounding smart contracts manifest in actual procurement processes. Design/methodology/approach:To investigate the application of smart contracts in healthcare procurement, we employed a qualitative research approach. This encompassed an extensive literature review of academic papers, industry reports, and relevant articles, uncovering the unique advantages and challenges in procurement & supply chain management. Building upon these findings, we developed a groundbreaking smart contract-based procurement system, presented through sequence diagrams. To validate our solution, we implemented and rigorously tested it within a real-world Ethereum environment. Findings:Our research reveals that integrating smart contracts into procurement processes results in streamlined operations, diminished reliance on intermediaries, and heightened transparency and traceability. Moreover, the proposed solution showcases significant potential for enhancing procurement efficiency in the healthcare sector. Research, Practical & Social Implications: These findings present valuable insights with far-reaching implications and hold substantial implications for stakeholders. Healthcare organizations can harness smart contracts to optimize their procurement procedures, yielding improved efficiency, transparency, and security. In a rapidly digitalizing landscape, our research empowers companies to maintain their competitive edge while delivering enhanced value to partners and customers. Originality/value:This article contributes significantly to the existing literature by offering a comprehensive examination of smart contract integration within procurement function, set within the broader context of supply chain management. It introduces a pioneering solution and provides a validated methodology, paving the way for in-depth exploration of smart contracts' impact on diverse stakeholders across healthcare supply chains
Ethereum is a decentralized digital currency andplatform blockchain which has gained popularity in Indonesia. Crypto assets have inherent risks, such as price volatility and the potential for fraud or hacking which is of course very detrimental to their users. Based on this, it is necessary to explore the accountability of parties using crypto assetsEthereum as an object of trade. The type of research used is normative juridical with an approach taken to statutory regulations. The results of the research show that liability can occur for default, unlawful acts, and unlawful acts. The form of liability that can be taken is in the form of cancellation of the agreement to claims for compensation by the aggrieved party. The government, in this case, needs to buy educational outreach to the public on the wise use of crypto assets.
The popularity of blockchain continues to increase as technology develops, especially in the context of Ethereum as one of the leading blockchain platforms. However, this increase was also followed by many cases of fraud, especially in the form of tokens. In blockchain technology, tokens often refer to cryptocurrencies or digital currencies used as a means of exchange related to a particular project or platform. This research designs and builds an application system that can detect scam crypto tokens on the Ethereum blockchain, specifically for the ERC-20 (Ethereum Request for Comments 20) token type, which was proposed by Fabian Vogelsteller in November 2015, is a token standard that implements APIs for tokens. in Smart Contracts. Making a scam detection application implements the deep learning method with the Deep Neural Network (DNN) algorithm and evaluates performance using two test scenarios by dividing the dataset into three ratios of training data and test data. The output of the application is JSON-RPC which is integrated with the website. In testing the DNN model, using 80% training data and 20% test data, the DNN algorithm provides an accuracy of 0.997558%. Furthermore, system testing was carried out involving various scenarios to verify its functionality, including input validation, data extraction, DNN prediction, and display of prediction results, which gave good results from the system created. The application has succeeded in identifying scam tokens with high accuracy. , increasing user security in crypto transactions.
Muhammad Haziq Abdul Hadi, Nor Azuana Ramli, Qamar UI Islam
Predicting future prices of cryptocurrencies, including Bitcoin and Ethereum, presents a formidable challenge owing to their inherent volatility. This study applies Long Short-Term Memory (LSTM), a well-established recurrent neural network for time series forecasting, to predict Bitcoin and Ethereum values. Historical price data for both cryptocurrencies, sourced from Yahoo Finance, serves as the basis for analysis. The dataset undergoes an 80% training and 20% testing partition. Subsequently, LSTM models are developed and trained on both datasets. In parallel, the gated recurrent unit (GRU), recognized as an advanced variant of the LSTM model, is explored for comparative purposes. Performance evaluation utilizes fundamental metrics, including root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). The results reveal an intriguing trend: both models exhibit superior performance when applied to the Ethereum dataset compared to the Bitcoin dataset. This observation suggests the potential presence of Ethereum-specific features or patterns that align more effectively with deep learning model architectures. Notably, the GRU model consistently outperforms the LSTM model across RMSE, MAE, and MAPE. These outcomes underscore the GRU model’s capacity as a robust tool for cryptocurrency value prediction. In summary, this study tackles the challenge of cryptocurrency price prediction while emphasizing the promising role of advanced neural network architectures, such as GRU, in enhancing prediction accuracy, thus offering valuable insights into financial forecasting.
This article explores the extent to which network activity can explain changes in Ethereum transaction fees. Such fees are referred to as “gas prices” within the Ethereum blockchain, and are important inputs not only for executing transactions, but also for the deployment of smart contracts within the network. Using a bootstrapped quantile regression model, it can be shown that network activity, such as the sizes of blocks or the number of transactions and contracts, can have a heterogeneous relationship with gas prices across periods of low and high gas price changes. Of all the network activity variables examined herein, the number of intraday transactions within Ethereum’s blockchain is most consistent in explaining gas fees across the full distribution of gas fee changes. From a statistical perspective, the bootstrapped quantile regression approach demonstrates that linear modeling techniques may yield but a partial view of the rich dynamics found in the full range of gas price changes’ conditional distribution. This is an important finding given that Ethereum’s blockchain has undergone fundamental economic and technological regime changes, such as the recent implementation of the Ethereum Improvement Proposal (EIP) 1559, which aims to provide an algorithmic updating rule to estimate Ethereum’s “base fee”.
Stefan Kambiz Behfar, Richard Mortier, Jon Crowcroft
The rapid growth of the Ethereum network necessitates advanced anomaly detection techniques to enhance security, transparency, and resilience against evolving malicious activities. While there have been significant strides in anomaly detection, they often fall short in capturing the intricate spatial-temporal patterns inherent in blockchain transactional data. This study presents a scalable framework that integrates Graph Convolutional Networks (GCNs) with Temporal Random Walks (TRW) specifically designed to adapt to the complexities and temporal dynamics of the Ethereum transaction network. Unlike traditional methods that focus on detecting specific attack types, such as front-running or flash loan exploits, our approach targets time-sensitive anomalies more broadly—detecting irregularities such as rapid transaction bursts, anomalous token swaps, and sudden volume spikes. This broader focus reduces reliance on pre-defined attack categories, making the method more adaptable to emerging and evolving malicious strategies. To ground our contributions, we establish three theoretical results: (1) the effectiveness of TRW in enhancing GCN-based anomaly detection by capturing temporal dependencies, (2) the identification of weight cancellation conditions in the anomaly detection process, and (3) the scalability and efficiency improvements of GCNs achieved through probabilistic sampling. Empirical evaluations demonstrate that the TRW-GCN framework outperforms state-of-the-art Temporal Graph Attention Networks (TGAT) in detecting time-sensitive anomalies. Furthermore, as part of our ablation study, we evaluated various anomaly detection techniques on the TRW-GCN embeddings and found that our proposed scoring classifier consistently achieves higher accuracy and precision compared to baseline methods such as Isolation Forest, One-Class SVM, and DBSCAN, thereby validating the robustness and adaptability of our framework.
More and more IoT use cases require trustworthy computing from cloud/back-end services, which cannot necessarily provide a fully trusted execution environment, data immutability, and traceability. The integration of IoT with the blockchain technology is one of the most promising solutions to achieve the previously mentioned features in the IoT networks. Researchers are also interested in integration solutions, and several solutions are already present in the scientific literature. However, there are still some uncertainties in establishing a direct and effective interaction between an IoT device and the given blockchain. In this work, we propose the first IoT hardware architecture model designed to accelerate time-consuming operations of IoT-Blockchain. The proposed IoT hardware architecture model is programmed in SystemC-TLM and can provide a significant reduction in execution time, 53% and 18% when running Hyperledger Sawtooth and Ethereum applications, respectively.
This article conducts an extensive examination of the persisting challenges related to smart contract attacks within blockchain networks, with a particular focus on the reentrancy attack. It emphasizes the inherent vulnerabilities embedded in the programming languages commonly employed for smart contract development, particularly within Ethereum Virtual Machine (EVM)-based blockchains. While the concrete example used primarily employs the Solidity programming language, the insights garnered from this study are readily generalizable to a wide array of blockchain architectures. Significantly, this article extends beyond the mere identification of vulnerabilities and ventures into the realm of proactive security measures. It explores the adaptation and adoption of dataflow programming paradigms, employing Domain-Specific Languages (DSLs) to enforce security by design in the context of smart contract development. This forward-looking approach aims to bolster the foundational principles of blockchain security, offering a promising research direction for mitigating the risks associated with smart contract vulnerabilities. The objective of this article is to cater to a diverse audience, ranging from individuals with limited computer science and programming expertise to seasoned experts in the field. It provides a comprehensive and accessible resource for fostering a deeper understanding of the intricate dynamics between blockchain technology and the imperative need for secure smart contract development practices.
Financial organizations are adopting an Open Bankings (OB) pattern for creativity in services and incorporation. OB permits Third-Party Servicing Providers (TSPs) to entry consumer financial data to identify optimal offers and enhance customer service. The reliance on third parties essential for the functioning of the OB environment prompts inquiries about digital identity incorporation, information exchange, and the safeguarding of secrets. Distributed Applications (DApps) for data classification and secret protection, user consent capturing, tracking TSP accessibility actions, controlling Applications Programming Interfaces (APIs), and maintaining Self-Sovereign Identity (SSI) are available; however, they have not yet been incorporated into a functional three-phase OB strategy. Upon evaluating the principal needs of main OB users, the research formulated a Blockchain-oriented Identity Managing and Access Controlling System (BIMACS) that incorporates benefits from both conventional banking and blockchain-based technologies. The BIMACS facilities utilize smart agreements and an undocumented authenticating system to establish a transaction security management system, which provides features such as distributed Third-Party Logins (TPL), OB account creation, information authorization, incorporated transactions, and TSP access tracking. The system efficiency study's findings demonstrate that the suggested structure's frequently executed functions incur a lower computational price than the median operations price on open Ethereum.
Kripto para piyasasının bilhassa son dönemlerde artan popülaritesi, piyasaların etkinliği ve geleceğe dair fiyat hareketlerinin anlaşılmasına yönelik ilgiyi de tetiklemişti. Bu çalışma, en yüksek işlem hacmine sahip 7 kripto para biriminin (Bitcoin (BTC), Binance Coin (BNB), Cardano (ADA), Dogecoin (DOGE), Ethereum (ETH), Tether (USDT) ve Rippel (XRP)) piyasa üzerindeki etkinliğini Fama'nın (1970) etkin piyasalar hipotezi çerçevesinde incelemekte ve bu kripto paraların birim kök ve durağanlık yapılarına odaklanarak, piyasa üzerindeki etkinlik düzeyini anlamak ve gelecekteki fiyat hareketlerine dair bulgular elde etmektir. Bu sayede kripto para piyasalarında etkinliği daha iyi anlamak ve yatırımcılar için daha güvenilir yatırım stratejileri oluşturmak için sağlam bir temel sunmak mümkün hale gelebilmektedir. Araştırmada incelenen 7 kripto paranın fiyat davranışlarını analiz etmek amacıyla birbirine göre farklı avantajları ve bulunan farklı panel birim kök testlerinden faydalanılarak güvenilir ve sağlam sonuçlara ulaşma ihtimali arttırılmıştır. Analitik tekniklerden elde edilen bulgular araştırmanın anakütlesini oluşturan 7 kripto paranın rassal yürüş sürecine tabi olmamak (durağan bir sürece karşılık gelmek) suretiyle, zayıf formda etkin olmadığını göstermektedir. Kripto para piyasalarındaki etkinliğin anlaşılması, yatırımcıların daha bilinçli kararlar almasına yardımcı olacak ve finansal riskleri daha etkin bir şekilde yönetmelerini sağlayacaktır.
Blockchain technology, the bedrock of cryptocurrency, has evolved beyond its initial scope, paving the way for a plethora of decentralized, secure applications.The anticipation surrounding blockchain's potential to become the dominant technology orchestrating online transactions is growing, due to its ability to provide efficient and secure solutions for a diverse range of applications on a global scale.This study delves into the potential benefits of deploying blockchain technology in the realm of crowdfunding.In recent years, crowdfunding has emerged as an alternative route for startups to garner funds, presenting a less bureaucratic and simpler process.The conventional crowdfunding model entails a collective of individuals contributing minor sums to support a project or start-up, with the crowdfunding platform earning a commission to coordinate the needs of both funders and fundraisers.Nonetheless, blockchain technology could potentially enhance the crowdfunding process by introducing a decentralized, tamperproof system comprised of interconnected nodes, thereby bolstering transparency, trust, efficiency, and convenience.To realize this potential, this paper proposes the application of Ethereum smart contracts to tackle prevalent issues in both Donation-Based and Equity-Based crowdfunding models.By adopting this approach, we hope to bring about greater transparency and efficiency to the crowdfunding process, thereby fostering an environment of trust that may catalyze further innovation in this space.
Long He, Xiangfu Zhao, Yichen Wang, Jiahui Yang · 5 authors
Security incidents in smart contracts still occur frequently, as the underlying code is often vulnerable to attacks. However, traditional methods to detect vulnerabilities in smart contracts are limited by certain rigid rules, reducing accuracy and scalability. In this work, we propose GraphSA, which combines Graph neural networks (GNNs) and Static Analysis for smart contract vulnerability detection. First, we present the contract tree, which is obtained by converting the control flow graph (CFG) of a smart contract. Each node in the tree represents a crucial operation code (opcode) block, and each edge represents the control flow (execution order) between code blocks. Then, we propose an extended SAGConv and Topkpooling graph neural network (ST-GNN) to learn the features of each node in the tree. To enhance detection accuracy, we eliminate and merge some non-crucial nodes to highlight key nodes and execution orders. Finally, we evaluate our approach on 7,962 real-world smart contracts running on Ethereum and compare it with state-of-the-art approaches on six types of vulnerabilities. Experimental results show that our approach achieves higher detection accuracy than others.
Temeljni cilj ovog rada je bio prikaz razvoja dviju kriptovaluta tokom povijesti te njihove sličnosti, razlike i primjena. Ethereum i Litecoin se, za razliku od Bitcoin-a, obrađuju puno brže. Znatiželja je došla u tome, kako i po čemu razlikujemo kriptovalute, naspram fizičkih valuta država. Svaka valuta ima različite aspekte, poput Ethereum-a. Osim generalnih transakcija Ethera (Ξ), valute Ethereum-a, također nudi i razmjenu raznih medija, poput slika, zvuka, i ostale vrste. Današnjim razmišljanjima, kriptovalute su još daleko od prihvaćanja većinu ljudi, uglavnom zbog nesigurnosti.
Timed data release refers to protecting sensitive data that can be accessed only after a pre-determined amount of time has passed. While blockchain-based solutions for timed data release provide a promising approach for decentralizing the process, designing an attack-resilient timed-release service that is resilient to malicious adversaries in a blockchain network is inherently challenging. A timed-release service on a blockchain network is inevitably exposed to the risk of post-facto attacks where adversaries may launch attacks after the data is released in the blockchain network. Existing incentive-based solutions for timed data release in Ethereum blockchains guarantee protection under the assumption of a fully rational adversarial environment in which every peer acts rationally. However, these schemes fail invariably when even a single participating peer node in the protocol starts acting maliciously and deviates from the rational behavior. In this paper, we propose a systematic solution for attack-resilient and practical blockchain-based timed data release in a mixed adversarial environment, where both malicious adversaries and rational adversaries exist. We first propose an effective uncertainty-aware reputation measure to capture the behaviors of the peer involved in timed data release activities in the network. In light of such a measure, we present the design of a basic protocol that consists of two critical ingredients, namely reputation-aware peer recruitment and verifiable enforcement protocols. The former, prior to the start of the enforcement protocols, performs peer recruitment based on the reputation measure to make the design probabilistically attack-resilient to the post-facto attacks. The latter is responsible for contractually guarding the recruited peers at runtime by transparently reporting observed adversarial behaviors. However, the basic recruitment design is only aware of the reputation of the peers and it does not consider the working time schedule of the participating peers and as a result, it results in lower attack-resilience. To enhance the attack resilience further without impacting the verifiable enforcement protocols, we propose a temporal graph-based reputation-aware peer recruitment algorithm that carefully determines the peer recruitment plan to make the service more attack-resilient. In our proposed approach, we formally capture the timed data release service as a temporal graph and we develop a novel maximal attack-resilient path-finding algorithm on the temporal graph for the participating peers. We implement a prototype of the proposed approach using Smart Contracts and deploy it on the Ethereum official test network, Rinkeby. For extensively evaluating the proposed techniques, we perform simulation experiments to validate the effectiveness of the reputation-aware timed data release protocols as well as our proposed temporal-graph-based improvements. The results demonstrate the effectiveness and strong attack resilience of the proposed mechanisms and our approach incurs only a modest gas cost.
Given the substantial volatility and non-stationarity of cryptocurrency prices, forecasting them has become a complex task within the realm of financial time series analysis. This study introduces an innovative hybrid prediction model, VMD-AGRU-RESVMD-LSTM, which amalgamates the disintegration–integration framework with deep learning techniques for accurate cryptocurrency price prediction. The process begins by decomposing the cryptocurrency price series into a finite number of subseries, each characterized by relatively simple volatility patterns, using the variational mode decomposition (VMD) method. Next, the gated recurrent unit (GRU) neural network, in combination with an attention mechanism, predicts each modal component’s sequence separately. Additionally, the residual sequence, obtained after decomposition, undergoes further decomposition. The resultant residual sequence components serve as input to an attentive GRU (AGRU) network, which predicts the residual sequence’s future values. Ultimately, the long short-term memory (LSTM) neural network integrates the predictions of modal components and residuals to yield the final forecasted price. Empirical results obtained for daily Bitcoin and Ethereum data exhibit promising performance metrics. The root mean square error (RMSE) is reported as 50.651 and 2.873, the mean absolute error (MAE) stands at 42.298 and 2.410, and the mean absolute percentage error (MAPE) is recorded at 0.394% and 0.757%, respectively. Notably, the predictive outcomes of the VMD-AGRU-RESVMD-LSTM model surpass those of standalone LSTM and GRU models, as well as other hybrid models, confirming its superior performance in cryptocurrency price forecasting.
The market for non-fungible token (NFT) art is expected to reach USD 44.2 billion in 2021 and increase by 67.57 percent in 2022, revolutionizing the relationship between artists, collectors, and investors. Despite this, concerns regarding the environmental impact of blockchain technology’s high energy consumption persist. NFT art transactions will continue to generate significant carbon emissions after Ethereum’s “Merge” to a Proof-of-Stake (PoS) system in September 2022, rendering many low-carbon solutions obsolete and necessitating further research into post-Merge alternatives. This study identifies solutions in the NFT art market, such as carbon neutrality, lazy minting, alternative consensus mechanisms, Layer 2 solutions and policy interventions. Carbon neutrality is achieved through investments in renewable energy or carbon credits to mitigate emissions generated by NFT art transactions. Lazy minting reduces energy consumption by postponing the creation of NFT art until a buyer is secured. In the NFT art ecosystem, alternative consensus mechanisms such as Proof of Authority (PoA) and Proof of Spacetime (PoST) reduce energy consumption. By offloading transactions from the primary blockchain, Layer 2 solutions enhance scalability and reduce energy consumption. Carbon taxes and energy consumption levies are examples of policy interventions that promote cleaner energy sources in the NFT art market. This study will explore the role of artists, collectors, galleries, and other significant players in encouraging environmentally sustainable practices in the NFT art market. In addition, it will investigate the effect of prominent NFT art sales on carbon emissions and the adoption of eco-friendly alternatives. By integrating and optimizing current carbon reduction strategies, the NFT art market can continue to flourish while reducing its environmental impact. The study emphasizes the significance of implementing a comprehensive strategy that incorporates multiple solutions that are tailored to the specific challenges of the NFT art market.
<p>The objective of the study is to use daily Thai data analysis to strengthen correlations between Bitcoin and conventional asset measurements. The most popular asset prices and indices include gold, oil, the SET50 index, Bitcoin (BTC), Ethereum (ETH), Litecoin (LTC), Ripple (XRP), Dashcoin (DASH), Stellar Lumens (XLM), Binance coin (BNB), and Dogecoin (DOGE). We find a significant correlation between cryptocurrencies and the digital economy using a matrix approach to the Pearson correlation coefficient. With the help of a minimal spanning tree model and random matrix theory, we can determine the shortest route between assets. Yet, as predicted, only a small percentage of the greatest eigenvalues diverge. We are also developing a novel technique to find the SET-50 index. In an investment portfolio during the coronavirus period, alternatives to the gold price and the DOGE may offer possibilities for risk diversification.</p>
Mikel Cortes-Goicoechea, Tarun Mohandas-Daryanani, José L. Muñoz, Leonardo Bautista-Gomez
Modern public blockchains like Ethereum rely on p2p networks to run distributed and censorship-resistant applications. With its wide adoption, it operates as a highly critical public ledger. On its transition to become more scalable and sustainable, shifting to PoS without sacrificing the security and resilience of PoW, Ethereum offers a range of consensus clients to participate in the network. In this paper, we present a methodology to measure the performance of the consensus clients based on the latency to receive messages from the p2p network. The paper includes a study that identifies the incentives and limitations that the network experiences, presenting insights about the latency impact derived from running the software in different locations.
Purpose The study was done to review the existing literature available on the theme using a popular technique known as a bibliometric review. The purpose was to explore important bibliometric trends such as geographical distribution of research; the most relevant countries and institutions and important collaboration networks, frequently published authors, the most relevant topics/research domains and relationships among these, average citations or per year, the most relevant sources, top authors’ production, authors’ impact by H index and the progression of important keywords over a period of time. Design/methodology/approach The study analyzed literature published in the English language from 2012 onwards that used the words “cryptocurrency”, “Ethereum” “Bitcoin” along with “investment/s” or “speculation/s” in the Title/ABS/KEY. A specialized approach was followed to retrieve and analyze focused research. The data for analysis was extracted from the Scopus database and was analyzed using Biblioshiny and VOSViewer. Findings The study found that the countries such as the UK, Australia, China and the USA have special relevance in terms of the number of citations and collaboration networks. Cryptocurrency/Cryptocurrencies, bitcoin have been the base themes along with other crucial issues such as volatility, hedging, COVID-19 pandemic, Ethereum, blockchain, co-integration, portfolio diversification/optimization, spillover, safe haven, investor attention, gold, etc. There is a lot of interdisciplinary research on the theme. Originality/value The current study used a concentrated approach to study the bibliometric literature about the financial implications of cryptocurrency as an asset class and not prominently its technological or legal aspects.