In today's globalized world, there is no transparency in exchanging data and information between producers and consumers. However, these tasks experience many challenges, such as administrative barriers, confidential data leakage, and extensive time delays. To overcome these challenges, we propose a decentralized, secured, and verified smart chain framework using Ethereum Smart Contract which employs Inter Planetary File Systems (IPFS) and MongoDB as storage systems to automate the process and exchange information into blocks using the Tendermint algorithm. The proposed work promotes complete traceability of the product, ensures data integrity and transparency in addition to providing security to their personal information using the Lelantos mode of shipping. The Tendermint algorithm helps to speed up the process of validating and authenticating the transaction quickly. More so in this time of pandemic, it is easier to meet the needs of customers through the Ethermint Smart Chain, which increases customer satisfaction, thus boosting their confidence. Moreover, Smart contracts help to exploit more international transaction services and provide an instant block time finality of around 5 sec using Ethermint. The paper concludes with a description of product storage and distribution adopting the Ethermint technique. The proposed system was executed based on the Ethereum-Tendermint Smart chain. Experiments were conducted on variable block sizes and the number of transactions. The experimental results indicate that the proposed system seems to perform better than existing blockchain-based systems. Two configuration files were used, the first one was to describe the storage part, including its topology. The second one was a modified file to include the test rounds that Caliper should execute, including the running time and the workload content. Our findings indicate this is a promising technology for food supply chain storage and distribution.
Pedro Raffy Vartanian, Álvaro Alves de Moura, Joaquim Carlos Racy, Roberto Simioni Neto
In May 2014, the animation “Quantum” was the first work to be associated with a non-fungible token (NFT) type certificate. As of 2020, the market has evolved considerably, with the millionaire figures and exponential growth typical of new disruptive technologies. Considering the recent rise of the NFT market, it is important to understand how it works and, above all, the determinants of the prices of NFTs are highlighted. Based on a detailed analysis of this new market, a GARCH multivariate econometric model is applied in order to assess whether it is possible to identify the price determinants of NFTs, based on the behavior of the prices of cryptocurrencies (Bitcoin and Ethereum), the US interest rate and the price of gold. The research is based on the study by Dowling (2022a), which sought to analyze relations between the prices of NFTs and cryptocurrencies. The results found coincide with the prices of NFTs that are similar and independent of cryptocurrencies, the interest rate and the price of gold, some specific differences to identify a determined period.
En este artículo realizamos un estudio acerca de uno de los temas más disruptivos en los últimos tiempos: las criptomonedas. Ya sea como una revolución en los métodos de pago, por ser un activo especulativo o por las particularidades de la tecnología en la que se soporta, las criptomonedas cada vez están generando un mayor impacto en nuestras vidas, especialmente, en el mundo de las finanzas. El principal objetivo que nos planteamos es adentrarnos en el conocimiento de las criptomonedas y la tecnología que las sostiene, de manera que nos permita tener una idea clara de las consecuencias de su aparición no solo en el ámbito financiero, sino también en la economía en general. Si realizamos una aproximación a estas criptomonedas y a la tecnología blockchain, podremos conocer las ventajas de su utilización, así como de las desventajas de su existencia, lo que nos permitirá tener conciencia del papel que puede desempeñar esta tecnología en el presente, a corto y largo plazo, especialmente en el ámbito empresarial. Tras este análisis de los orígenes del Bitcoin y su evolución, estudiamos su correlación con varios activos e índices: Ethereum (segunda criptomoneda con mayor valor), oro (commodity considerada como valor refugio), S&P500 (índice más representativo del mercado estadounidense) y el MSCI World Index (índice más representativo a nivel global), explicando brevemente cada uno de estos activos, para finalmente analizar su correlación con el Bitcoin desde el año del que disponemos datos de cada uno.
Tim Tanggap Insiden Siber Spanyol pada tahun 2021 menyatakan bahwa penggunaan mata uang kripto mengalami pertumbuhan. Hal ini selaras dengan indikasi pertumbuhan tindak kejahatan cryptojacking yang memanfaatkan mata uang kripto. Penambangan mata uang kripto memerlukan sumber daya yang besar. Terindikasi adanya oknum yang melakukan aktivitas penambangan menggunakan sumber daya ilegal, dan aktivitas ini dapat dikatakan sebagai tindak kejahatan cryptojacking. Berdasarkan sistem indexing Scopus, pada rentang tahun 2018 - 2021 terdapat 94 artikel penelitian terkait tindak kejahatan cryptojacking. Negara yang cukup aktif melakukan penelitian tindak kejahatan cryptojacking adalah Tiongkok, India, dan Amerika Serikat. Untuk memperdalam kajian tindak kejahatan cryptojacking, diperlukan pengetahuan tentang keamanan sistem dan jaringan komputer, malicous software (malware), dan mata uang elektronik seperti bitcoin, ethereum, litecoin, dan sebagainya. Dengan menggunakan analisis bibliometrika, dapat dilakukan kajian lebih lanjut menggunakan kata kunci atau tren dari sebuah topik penelitian. Selain itu, hal ini mempermudah peneliti dalam melakukan pemetaan topik-topik lanjutan.
It is possible to download a piece of software over the internet and then verify its correctness locally using an appropriate trusted proof system. However, on a blockchain like Ethereum, smart contracts cannot be altered once deployed. This guarantee of immutability makes it possible for end users to interact collectively with a 'networked' piece of software, with the same opportunity to verify its correctness. Formal verification of smart contracts on a blockchain therefore offers an unprecedented opportunity for end users to collectively interact with a deployed instance of software that they can verify while not relying on a central authority. All that is required to be trusted beyond the blockchain itself is an appropriate proof system, a component which always needs to be in the trusted computing base, and whose rules and definitions can be public knowledge. DeepSEA (Deep Simulation of Executable Abstractions) could serve as such a proof system.
To create trustworthy programs, the 'gold standard' is specifications at a high-enough level to clearly correspond to the informal specifications, and also a refinement proof linking these high-level specifications down to, in our case, executable bytecode. The DeepSEA system demonstrates how this can be done, in the context of smart contracts on the Ethereum blockchain. A key component of this is the model of the blockchain on which the smart contracts reside. When doing proofs in DeepSEA, it is critical to have such a model, which allows for the writing of specifications at a high-level clearly corresponding to informal specifications. A candidate model for doing so and its usefulness for carrying out proofs is discussed in this paper.
Miguel Sánchez‐de la Rosa, Carlos Núñez‐Gómez, Blanca Caminero, Carmen Carrión
Summary Fog computing has become a complementary technology to cloud computing and addresses some of the cloud computing threats such as the response time and network bandwidth demand. Fog computing successes processing data and storing data near to the edge, and usually is combined with container virtualization to provide hardware isolation. Empowered by these capabilities, numerous Internet of Things (IoT) applications are developed as virtualized instances on resource‐constrained fog nodes such as single‐board computers (SBC). In addition, blockchain has emerged as a key technology that is transforming the way we share information. Blockchain technology represents a decentralised, distributed, and immutable database ledger and is a potential solution for the distributed ecosystem of IoT applications. The distributed structure of blockchain is naturally suitable for IoT applications. However, it introduces new challenges related to CPU overhead or response time. This paper proposes a layered architecture that integrates blockchain technology and OS‐level virtualization technology to develop fog‐based IoT applications. It also provides insights for future deployments through a proof‐of‐concept use case harnessing SBCs, in this case Raspberry Pi, as blockchain‐enabled fog nodes to drive virtualized IoT applications. The study shows that the maximum CPU overhead added by a permissioned blockchain based on Ethereum on the Raspberry Pi is around a 25% under stress situations while the overhead introduced by the sealer process is negligible. These results support the feasibility of using blockchain on resource‐constrained fog nodes for supporting IoT applications.
We applied the SVAR-LiNGAM to illustrate the causal relationships between the spot exchange rate, and three crypto-asset exchange rates, Bitcoin, Ethereum, and Ripple. It was notable that the causal order, the EUR_USD spot rate->Bitcoin->Ethereum->Ripple, was obtained by this approach. All the instantaneous effects were strongly positive. Moreover, it was notable that Bitcoin can influence the EUR_USD spot rate positively with a one-day time lag.
Lucas Penteado Lopes da Silva, Luiz Adeildo da Silva, Josafat Marinho Falcão Neto, Geidson Benício Coelho de Souza
Elaboração de algoritmos de aprendizado de máquina para a previsão do comportamento de preços da criptomoeda Ethereum, utilizando-se uma base de dados pública (Kaggle). Os modelos elaborados foram do tipo linear (ARIMA, séries temporais) e nãolinear (três modelos de redes neurais LTSM). Como melhor resultado, verificou-se que um dos modelos não lineares foi capaz de realizar previsões distantes em média de 4,32% dos preços reais.
Blockchain technology has changed how people think about how they used to store and trade their assets, as it introduced us to a whole new way to transact: using digital currencies. One of the major innovations of blockchain technology is decentralization, meaning that traditional financial intermediaries, such as asset-backed security issuers and banks, are eliminated in the process. Even though blockchain technology has been utilized in a wide range of industries, its most prominent application is still cryptocurrencies, with Bitcoin being the first proposed. At its peak in 2021, the market cap for Bitcoin once surpassed 1 trillion US dollars. The open nature of the crypto market poses various challenges and concerns for both potential retail investors and institutional investors, as the price of the investment is highly volatile, and its fluctuations are unpredictable. The rise of Machine Learning, and Natural Language Processing, in particular, has shed some light on monitoring and predicting the price behaviors of cryptocurrencies. This paper aims to review and analyze the recent efforts in applying Machine Learning and Natural Language Processing methods to predict the prices and analyze the behaviors of digital assets such as Bitcoin and Ethereum.
Investments in cryptocurrencies (CCs) remain risky due to high volatility. Exchange Traded Funds (ETFs) are a suitable tool to diversify risk and to benefit from the growth of the whole CC sector. We construct an ETF on the CRIX, the CRyptocurrency IndeX that maps the non-stationary CC dynamics closely by adapting its constituents weights dynamically. The scenario analysis considers the fee schedules of regulated CC exchanges, spreads obtained from high-frequency order book data, and models capital deposits to the ETF stochastically. The analysis yields valuable insights into the mechanisms, costs and risks of this new financial product: i) although the composition of the CRIX ETF changes frequently (from 5 to 30 constituents), it remains robust in its core, as the weights of Bitcoin (BTC) and Ethereum (ETH) are robust over time, ii) on average, a portion of 5.2% needed to be rebalanced at the rebalancing dates, iii) trading costs are low compared to traditional assets, iv) the liquidity of the CC sector has increased significantly during the analysis period, spreads occur especially for altcoins and increase by the size of the transactions. But since BTC and ETH are most affected by rebalancing, the cost of spreads remains limited.
Rodrigo Dutra Garcia, Gowri Ramachandran, Raja Jurdak, Jó Ueyama
Real-world applications in healthcare and supply chain domains produce, exchange, and share data in a multi-stakeholder environment. Data owners want to control their data and privacy in such settings. On the other hand, data consumers demand methods to understand when, how, and who produced the data. These requirements necessitate data governance frameworks that guarantee data provenance, privacy protection, consent management, and selective disclosure. We introduce a decentralized data governance framework based on blockchain technology, proxy re-encryption, and Boneh, Boyen, and Shacham (BBS) signatures to let data owners control, selectively share and track their data through privacy-enhancing, consent management, and selective disclosure mechanisms. Besides, our framework allows the data consumers to understand data lineage through a blockchain-based provenance mechanism. We use Digital medical e-prescription as the use case since it handles sensitive data in a multi-stakeholder environment while showing how the medical community can manage patients’ sensitive prescription data, involving patients as data owners, and doctors, and pharmacists as data consumers. Our proof-of-concept implementation and evaluation results based on CosmWasm, Hyperledger Besu, Ethereum, pyUmbral PRE, and BBS signatures show that the proposed decentralized system is platform-agnostic, scalable and guarantees a higher degree of transparency, privacy, and trust with minimal overhead.
Non-fungible tokens (NFTs) are unique digital items with blockchain managed ownership. Ethereum blockchain based smart contract created the environment for NFTs (ERC721) to reach its one of the most important future application domains. Non fungible tokens got more attention when the market saw record breaking sales in 2021. Virtually anything of value can be traced and traded on the blockchain network by minting them as NFTs. NFTs provide the users with a decentralized proof of ownership representation, as every transaction and trade of NFTs gets recorded in the Ethereum network blocks. The value of NFTs is derived from their being non fungible meaning that the token cannot be replaced with an identical token (giving it inherent scarcity). In this paper, we study the growth rate and evolutionary nature of the NFT network and try to understand the NFT ecosystem. We explore the evolving nature of the NFT interaction network from a temporal graph perspective. We study the growth rate and observer the semantics of the network. Here on the observer network, we will run two graph algorithms on the dataset. Lastly, observe and forecast the survival of NFTs bubble by applying the Logarithmic periodic power law (LPPL) model to the time series data on one of the most famous NFT collections CryptoPunks (predicting price increase), which has seen sales of around $23.7 million around mid of 2021.
В ходе исследования проведен анализ рынка криптовалют для выбора более актуальной монеты с большой капитализацией. Благодаря своей набирающей популярности для исследования была выбрана криптовалюта ETH. Литературный обзор подтвердил актуальность и показал невысокий уровень изученности этой криптовалюты в рамках темы. Рассмотрены значения ряда за период с 14.03.2020 по 30.06.2020. Проведены тесты стационарности с помощью анализа графиков АКФ и ЧАКФ выбранного ряда криптовалюты, а также расширенные тесты Дики-Фуллера. Преобразованы исходные данные для уменьшения разброса дисперсии (логарифмирование) и исключение сезонности и тренда (взятие второй разности). Выполнено построение моделей прогноза с помощью модели авторегрессии — проинтегрированного скользящего среднего ARIMA (p, d, q) и с помощью статистических методов (анализ обычного и скорректированного коэффициентов детерминации) и информационных критериев Акайке и Швардца была выбрана единственная подходящая модель. Для того, чтобы можно было прогнозировать значения, была проведена диагностика модели по трем параметрам (остатки белый шум; модель стационарна и обратима) с помощью анализа остатков и теста единичного корня, и после получения успешного результата, спрогнозированы 2 значения за период с 01.07.2020 по 02.07.2020. Сравнение реальных и прогнозных значений цены криптовалюты показало, что сумма квадратов отклонений существенно ниже от выбранной точности результатов. Визуальный анализ графика также показал: модель способна формировать пики и спады цены. Был сделан вывод об эффективности данного метода в краткосрочных прогнозах криптовалюты. The study conducts an analysis of the cryptocurrency market to select a more relevant coin with a large capitalization. Due to its gaining popularity, the cryptocurrency ETH was chosen for the study. Literature review confirms the relevance and shows a low level of study of this cryptocurrency within the topic. The values of the series for the period from 14.03.2020 to 30.06.2020 are considered. The stationarity tests with the analysis of ACF and ChakF charts of the selected cryptocurrency series, as well as the extended Dickey-Fuller tests are carried out. The initial data were transformed to reduce dispersion (logarithm) and to exclude seasonality and trend (taking the second difference). Forecasting models were built by means of autoregressive model — ARIMA (p, d, q) integrated moving average and by means of statistical methods (analysis of ordinary and adjusted coefficients of determination) and Akaike and Schwartz information criteria the only appropriate model was selected. In order to be able to predict values, the model was diagnosed by three parameters (residuals white noise; the model is stationary and reversible) using residuals analysis and unit root test, and after obtaining a successful result, 2 values were predicted for the period from 01.07.2020 to 02.07.2020. A comparison of the real and predicted cryptocurrency price values shows that the sum of squares of deviation is significantly lower than the selected accuracy of the results. Visual analysis of the chart also shows that the model is able to form price peaks and declines. It is concluded that this method is effective in short-term cryptocurrency forecasts.
This paper inquires into the dynamic imaginaries of the Ethereum project. We present Ethereum as animated by three such imaginaries: the world computer (technical), productive money (economic) and public goods (political). We examine how these imaginaries are materialized, carried forward and evolve through the Ethereum ecosystem, focusing on how Ethereum’s prefigurative logic underpins this dynamism. In our analysis, we pay particular attention to how the imaginaries overlap and often generate contradictions that nonetheless do not seem to undermine the cohesion of the project. We introduce the concept of ‘prefigurative imaginaries’ to describe how prefiguration works to create multiple, mutually entangled but distinct imaginaries.
Miguel Pincheira, Elena Donini, Massimo Vecchio, Salil S. Kanhere
The data economy is based on data and information sharing and tremendously impacts society as it facilitates innovative collaborations and decision-making strategies. Nonetheless, most dataset-sharing solutions rely on a centralized authority that rules data ownership, availability, and accessibility. Recent works have explored the integration of distributed storage and blockchain to enhance decentralization, data access, and smart contracts for automating the interactions between actors and data. However, current solutions propose a smart contract design limiting the system's scalability in terms of actors and shared datasets. Furthermore, little is known about the performance of these architectures when using distributed storage instead of centralized storage approaches. This paper proposes a scalable architecture called DeBlock for data sharing in a trusted way among unreliable actors. The architecture integrates a public blockchain that provides a transparent record of datasets and interactions, with a distributed storage for data storage in a completely decentralized way. Furthermore, the architecture provides a smart-contract design for a transparent catalog of datasets, actors, and interactions with efficient search and retrieval capabilities. To assess the system's feasibility, robustness, and scalability, we implement a prototype using the Ethereum blockchain and leveraging two decentralized storage protocols, Swarm and IPFS. We evaluate the performance of our proposed system in different scenarios (e.g., varying the amount and size of the shared datasets). Our results demonstrate that our proposal outperforms benchmarks in gas consumption, latency, and resource requirements, especially when increasing the number of actors and shared datasets.
Malware detection approaches have been extensively studied for traditional software systems. However, the development of blockchain technology has promoted the birth of a new type of software system–decentralized applications. Composed of smart contracts, a type of application that implements the Ponzi scheme logic (called smart Ponzi schemes) has caused irreversible loss and hindered the development of blockchain technology. These smart contracts generally had a short life but involved a large amount of money. Whereas identification of these Ponzi schemes before causing financial loss has been significantly important, existing methods suffer from three main deficiencies, i.e., the insufficient dataset, the reliance on the transaction records, and the low accuracy. In this study, we first build a larger dataset. Then, a large number of features from multiple views, including bytecode, semantic, and developers, are extracted. These features are independent of the transaction records. Furthermore, we leveraged machine learning methods to build our identification model, i.e., Mul ti-view Cas cade Ensemble model (MulCas). The experiment results show that MulCas can achieve higher performance and robustness in the scope of our dataset. Most importantly, the proposed method can identify smart Ponzi scheme at the creation time.
Çalışmada, 2016-2022 yılları arasında blok zincir ve akıllı şehir konuları ile ilgili uluslararası alan yazınında yayınlanan çalışmaların bibliyometrik özelliklerinin belirlenmesi amaçlanmıştır. Scopus veri tabanı üzerinden “blockchain and smart city” anahtar kelime araması yapılmıştır. Arama yapılan tarihte toplam 1143 makaleye ulaşılmıştır. Araştırma kısıtlarında anahtar kelime bazında sadece “blockchain ve smart city” kelimelerinin birlikte geçtiği çalışmalar dikkate alınmış ve 329 çalışma ile analiz gerçekleştirilmiştir. Bibliyometrik analiz kapsamında öncelikle yayınların dağılımlarına ait genel bilgiler verilmiş, daha sonra birlikte bulunma (co-occurance) ve ortak atıf (co-citation) haritalamaları VOSviewer programı aracılığıyla görselleştirilmiştir. Çalışmanın sonuçları incelendiğinde; konuyla ilgili çalışmalara olan ilginin yıllar geçtikçe arttığı, en çok makale türünde yayın yapıldığı, konuyla ilgili literatüre en çok yayın yapılan ülke olarak Hindistan’ın katkıda bulunduğu, haritalama sonuçlarına göre ise ilk yıllarda bitcoin, ethereum gibi kavramlara öncelik verilirken zamanla bu kavramların yerini nesnelerin interneti (IoT), güvenlik (security) ve blok zincir teknolojileri gibi kavramların aldığı görülmüştür.
This study examines the behaviour of cryptocurrencies’ returns to stock market volatility and cybercrime in the South African economy. The study makes us Generalized Autoregressive Score Model (GAS) investigate the time-varying correlation between cryptocurrencies’ returns and cybercrime, and cryptocurrencies’ returns and stock market volatility by making use of daily time series data on different four types of Cryptocurrencies, Bitcoin, Ethereum, Tether and BNB from January 2019 to December 2021. The study also makes use of the regime-switching approach to regime-switching impacts on the cryptocurrencies’ returns. The empirical results obtained showed that cybercrime, on average, has negative impacts on the cryptocurrencies’ returns and the time-varying correlation between stock market volatility and each of the cryptocurrencies’ returns is largely positive. The stock market volatility impact is found to be regime-switching dependent. The study recommends that efforts to reduce cybercrime activities must be reinforced to deepen the use of digital currencies and policy measures must be taken to ensure reduced or moderate stock market volatility.
The existing studies rarely reveal the reasons for the digital currency price fluctuation from the perspective of internal interaction and contagion. Therefore, to fill this research gap, this paper comprehensively adopts the dynamic conditional correlation (DCC-) GARCH model and wavelet coherence analysis (WTC) to reveal the internal correlation and formation reasons of digital currency price fluctuations. Our research has the following findings: (1) the price fluctuations of digital currency are highly related. Through the observation of the dynamic conditional correlation coefficient graph, it is found that the price fluctuations have a strong time-varying trend, manifested as a ‘contagious’ characteristic. (2) During the outbreak of COVID-19, most digital currencies have shown positive resonance in the short, medium, and long term, suggesting that the COVID-19 pandemic has increased the correlation and contagion of digital currency price fluctuations. (3) In the short term, Bitcoin is the main ‘contagious source’ of digital currency price fluctuation. But in the medium and long term, Ethereum and Ripple, which are closely related to the real economy, have a greater impact and become the new ‘contagious source’. Generally speaking, Bitcoin, Ethereum, and Ripple are the internal causes of instability in the digital currency market. Finally, based on the empirical conclusion, this paper proposes that the digital currency portfolio should be optimized to meet the investment demand; strengthen digital currency regulatory cooperation, and improve regulatory efficiency. Let the digital currency return to the ‘currency’ attribute and serve the real economy.
Nowadays, finding genetic components and determining the likelihood that treatment would be helpful for patients are the key issues in the medical field. Medical data storage in a centralized system is complex. Data storage, on the other hand, has recently been distributed electronically in a cloud-based system, allowing access to the data at any time through a cloud server or blockchain-based ledger system. The blockchain is essential to managing safe and decentralized transactions in cryptography systems such as bitcoin and Ethereum. The blockchain stores information in different blocks, each of which has a set capacity. Data processing and storage are more effective and better for data management when blockchain and machine learning are integrated. Therefore, we have proposed a machine-learning-blockchain-based smart-contract system that improves security, reduces consumption, and can be trusted for real-time medical applications. The accuracy and computation performance of the IoHT system are safely improved by our system.
Open access
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
Normal cash has developed and appears numerous downsides such as inaccessibility. It is inclined to burglary and is intensely directed by government offices. Cryptocurrencies have risen as a egotistic money related framework. They depend upon secure disseminated ledger data structure. Mining plays a critical portion in this framework [1]. Basically, our cryptocurrency could be a conveyed database that keeps up tamper-proof information structure pieces containing his bunches of person exchanges. Blockchain innovation can be a widely emerging approach to data innovations. Bitcoin as a cryptocurrency has made several considerations since it was one of its earliest implementations. They discuss the key elements driving the development of sophisticated cryptocurrencies alongside Ethereum, a blockchain implementation with a focus on informed contracts [1]. In its most basic form, our cryptocurrency may be thought of as a distributed database that keeps track of tamper-proof data structure blocks comprising batchesof individual transactions [1].
Open access
Blockchain Technology Applications and Security
Internet Traffic Analysis and Secure E-voting
Advanced Steganography and Watermarking Techniques