Bitcoin Blockchain Research Articles

Assessment of Bitcoin carbon footprint

Bitcoin is a breakthrough financial technology but a volatile asset in financial markets with a complex fundamental consensus algorithm (Proof-of-Work) limiting its large-scale adoption due to environmental-related issues. Hitherto, the role of its technical and infrastructural composition that drives carbon footprint from an ecological perspective is rarely discussed in the literature. Here, we use machine learning and econometric techniques to analyze the past, present, and future changes in Bitcoin's carbon footprint with daily data spanning July 18, 2010 to December 04, 2021. We document technical drivers, decomposition effects, causal nexus, and implications of the Bitcoin blockchain's increasing energy and carbon footprint. We show that Bitcoin's technical drivers could have potential impacts on Bitcoin's carbon footprint, and subsequently, global climate change. For example, the network's hashrate increases mining difficulty––thereby increasing Bitcoin's energy consumption and subsequently, carbon footprint. We observed a direct association between the marginal effect of block size and transaction count––implying that a higher block size improves transaction efficiency and then reduces Bitcoin's energy and carbon footprint. Besides, low mining difficulty increases market capitalization whereas increasing mining difficulty reduces bitcoin mining profit in the long run. This infers the reward for mining Bitcoin has a diminishing return in the long term. Thus, the adoption of advanced hardware for Bitcoin mining will spur energy and carbon intensity, yet will have a low return on investment. We highlight environmental regulations and regulatory changes that could limit Bitcoin's carbon footprint.

Analysis of the confirmation time in proof-of-work blockchains

In blockchain networks driven by Proof of Work, clients spend a certain amount of cryptocurrency (called fees) to control the speed of confirmation of the transactions that they generate. In fact, transactions are confirmed according to a strong priority policy that favors those offering the highest fees. The problem of determining the optimal fee to offer to satisfy certain delay requirements is still widely open and, at the state of the art, mainly reactive methods based on historical data are available. In this work, we propose a queueing model based on the exact transient analysis of a M/MB/1 system to address this problem. The model takes into account (i) the state of the Mempool (the backlog of pending work) when the transaction is generated, (ii) the current transaction arrival intensity and (iii) the distribution of the fees offered by other transactions to the miners. We apply the model to study the performance of the Bitcoin blockchain. Its parameterization is based on an extensive statistical analysis of the transaction characteristics. To this aim, we collected data from over 1.5 million of pending transactions observed in the Mempool of our Bitcoin node. The outcome of our analysis allows us to provide an algorithm to quickly compute the expected transaction confirmation time given the blockchain state, and to highlight new insights on the relations between the transaction fees and confirmation time in BTC blockchain.

RESS: A Reliable and Effcient Storage Scheme for Bitcoin Blockchain Based on Raptor Code

RESS: A Reliable and Effcient Storage Scheme for Bitcoin Blockchain Based on Raptor Code

Analysis of POW in Bitcoin and POS in Peercoin

The technology of blockchain, which is the underlying technology of cryptocurrency like Bitcoin, has changed the world dramatically and it has fascinated researchers from all over the world. Originating from the Byzantine General Problem, the consensus mechanism is utilized to reach an agreement in a group where the channel for communication could be unreliable. Among all the consensus mechanisms in the blockchain, proof of stake (POS) and proof of work (POW) are the most widely used. POW was first used in the Bitcoin blockchain and soon became its central consensus mechanism of it and the earliest implementation of POS for cryptocurrency was Peercoin. Numerous research has been done in this field, but they failed to explain the features of the two mechanisms comprehensively. In this paper, the author did a brief introduction to the hash function and consensus mechanism as well as how POW and POS work, then made detailed analysis and comparisons of a POW in Bitcoin and POS in Peercoin in terms of fairness, security, environmental influence, penalty, negotiability and sustainability, and future trend, and declared that both of POS and POS had some advantages and drawbacks. The author also listed some risks and potential problems of POW and POS and then gave possible solutions for them.

Follow the Money: Analyzing Darknet Activity Using Cryptocurrency and the Bitcoin Blockchain

Transactions on the darknet are notoriously difficult to examine. Prior criminological research has generally used web scraping and qualitative text analysis to examine illegal darknet markets. One disadvantage of this process is that individuals can lie. Fortunately for researchers, the currency used for transactions on the darknet, cryptocurrency, is designed to be tracked. In this article, we examine transactions from a former darknet marketplace, AlphaBay. Using the blockchain, we examine the interconnectedness of both legal and illegal cryptocurrencies. In addition, we provide a structured approach to quantitatively examine the Bitcoin blockchain ledger, offering both the tools and our own experiences for other researchers interested in such approaches. While cybersecurity, information technology, accounting, and other disciplines can examine the financial data itself, we believe that criminologists can provide additional benefits in pattern analysis and organizing the context and theory around the transactions. Our results show that cryptocurrency transactions are generally identifiable (90%) and involve likely illegal transactions, transactions that attempt to obfuscate other transactions, and legal transactions. We end with a discussion of newer cryptocurrencies and related technology and how they will likely shape future work.

IoETTS: A Decentralized Blockchain-based Trusted Time-stamping Scheme for Internet of Energy

<p>As a new-style smart grid, Internet of Energy (IoE) is important and how to provide its trusted time-stamping service becomes a hit. For example, an energy provider needs to prove he/she transferred some energy to a consumer at some time. Nevertheless, traditional trusted time-stamping scheme with a central service provider is not suitable for IoE. Some researchers try to solve this problem via blockchain, due to its decentralization, traceability and tamper-proof. However, there are still chal­lenges when using blockchain. Some have to introduce another kind of central participant. Some have to face the problem of accuracy and availability when using the Bitcoin blockchain. Some have to generate too many extra transactions. To address the aforementioned problems, we propose a fully decentralized trusted time-stamping scheme without any central participant and fulfill six design goals. Compared with the state-of-the-art blockchain-based time-stamping scheme named Chronos, our scheme enjoys less cryptographic operations. We then tested our scheme in the development (local) network and two live networks of the Ethereum. The experiment shows that we have implemented a simple, effective, accurate and low-cost decentralized trusted time-stamping scheme.</p> <p> </p>

Towards Anonymous yet Accountable Authentication for Public Wi-Fi Hotspot Access with Permissionless Blockchains

Anonymous yet accountable authentication can protect users' privacy and security and prevent users from misbehaving when they access public Wi-Fi hotspots. However, most existing privacy-enhanced authentication schemes either do not meet the accountability requirements in public Wi-Fi hotspot access or they are inherently dependent on trusted third parties, and therefore are undeployable in practical settings. In this paper, we design and implement an access authentication scheme to simultaneously and efficiently provide anonymity and accountability without relying on any trusted third party by utilizing a permissionless blockchain (e.g., Bitcoin or Ethereum) and Intel SGX. Inspired by the recent progress on Bitcoin techniques such as Colored Coins, we utilize the unmodified Bitcoin blockchain as the powerful platform to manage access credentials without introducing any trusted third party. We leverage SGX-based mixer to allow users to anonymously exchange their access credentials and design the verification path of access credentials to support blacklisting misbehaving access credentials without compromising users' anonymity. By integrating with the anti-double-spending property of the Bitcoin blockchain, our scheme can simultaneously provide users' accountability and anonymity without involving any trusted third party. Finally, we demonstrate that our proposed scheme is compatible with the current Bitcoin system or other permissionless blockchains, and is highly effective and practical for public Wi-Fi hotspot access control systems.

Analysis of interaction between miner decision making and user action for incentive mechanism of bitcoin blockchain

In Bitcoin blockchain, miner nodes are likely to choose transactions with high fee to be included in a block. This makes transactions with high fee being processed fast, affecting the amount of transaction fee that users want to pay. The reward for a winning miner consists of transaction fee and newly issued coins, and hence the amount of newly issued coins also affects the miner decision to participate in the mining competition. In addition, mining reward also affects the total hash computing power, which plays an important role of Bitcoin security for reducing the success probability of security attack by a malicious miner. In this paper, we develop a mathematical model for analyzing the interaction between miner decision making and user actions in terms of transaction fees, transaction-confirmation time, and security. We analyze the transaction-inclusion process with queueing theory, while decision making processes of miners and users are analyzed in the context of Nash equilibrium. The numerical examples show how the mining costs and newly issued coins affect miner decision making.

Факторы доходности Ethereum как платформы для создания децентрализованных приложений

By now, cryptocurrencies have almost become a part of the modern financial asset space, but the cryptocurrency market itself is not homogeneous, and individual cryptocurrencies can differ significantly in their properties and functions. For example, the cryptocurrency Ether is second in capitalization after Bitcoin, but the Ethereum and Bitcoin blockchains differ significantly in their properties and functions. In particular, Ethereum is the most popular digital platform for creating decentralized applications (dApps). The purpose of this work is to try to answer the question "Does the market take into account the features of the Ethereum blockchain in the price dynamics of the Ether cryptocurrency?" This question is also directly related to the search for potential fundamental factors that can explain the price dynamics of Ether. The main econometric method used in the study is generalized autoregressive conditional heteroskedasticity (GARCH) models. Having evaluated about 15 thousand different specifications of GARCH models, where various Ethereum blockchain usage metrics were used as explanatory variables, we obtained the results that Ethereum network usage metrics do not significantly correlate with Ether cryptocurrency returns. Moreover, these metrics are also unable to explain the relative strengthening/weakening of Ether relative to Bitcoin. Thus, we conclude that despite the presence of a number of special functional properties of the Ethereum blockchain, the price dynamics of the Ether cryptocurrency does not reflect them.

Efficient blockchain addresses classification through cascading ensemble learning approach

Bitcoin is a pseudonymous, decentralised cryptocurrency that has become one of the most widely utilised digital assets to date. Because of its uncontrolled nature and Bitcoin users' inherent anonymity, it has seen a significant surge in its use for illegal operations. This necessitates the use of unique methods for categorising the addresses of Bitcoin users. This research classifies and predicts the portion of users' activities that are lawful and unlawful on the Bitcoin blockchain. The dataset contains almost 27 billion samples that are divided into nine user acts, five of which were unlawful. To predict cross-validation (CV) accuracy, ensemble learning algorithms are trained and tested. With cross-validation accuracy of 68.63% and 49.64%, respectively, gradient boosting emerged as the best ensemble learning algorithm for classification and prediction, while bagging emerged as the worst. To get the best classification and prediction, hyperparameter tuning is used to find the optimal parameters, which helped to enhance the cross-validation accuracy of the bagging algorithm to 67.70%, with moderate improvements in the rest of the learning algorithms.

Illicit Activity Detection in Bitcoin Transactions using Timeseries Analysis

A key motivator for the usage of cryptocurrency such as bitcoin in illicit activity is the degree of anonymity provided by the alphanumeric addresses used in transactions. This however does not mean that anonymity is built into the system as the transactions being made are still subject to the human element. Additionally, there is around 400 Gigabytes of raw data available in the bitcoin blockchain, making it a big data problem. HPCC Systems is used in this research, which is a data intensive, open source, big data platform. This paper attempts to use timing data produced by taking the time intervals between consecutive transactions performed by an address and make an identification of the nature of the address (illegal or legal). With the use of three different goodness of fit run tests namely Kolmogorov–Smirnov test, Anderson-Darling test and Cramér–von Mises criterion, two addresses are compared to find if they are from the same source. The BABD-13 dataset was used as a source of illegal addresses, which provided both references and test data points. The research shows that time-series data can be used to represent transactional behaviour of a user and the algorithm proposed is able to identify different addresses originating from the same user or users engaging in similar activity.

Maximizing portfolio profitability during a cryptocurrency downtrend: A Bitcoin Blockchain transaction-based approach

The volatile and unpredictable nature of the cryptocurrency market makes it particularly challenging to make profitable investment decisions. different machine learning-based techniques have been employed for forecasting cryptocurrency value. However, although some works have addressed incorporating the Blockchain transactions’ data into the analysis, none of them has provided a hybrid solution, including features obtained through complex network modeling. In this paper, we investigated the use of machine learning and complex network techniques to improve the profitability of a cryptocurrency portfolio during a downtrend period. We extracted features through a complex network-building methodology based on the Bitcoin blockchain transactions, merged them with the historical cryptocurrency values, and generated the predictions using different machine-learning models. The results indicated that incorporating complex network features improved the performance in retaining the initial capital at the end of the experiment, leading to an increment of 7.09% and 4.33% for the CNN and LSTM models, respectively. Our findings suggest that the proposed method enhanced the performance of cryptocurrency investment strategies during downtrend periods.

BLOCKCHAIN-BASED LEARNING MANAGEMENT SYSTEMS

In the context of the development of the world educational sphere, taking into account modern trends and challenges, in the context of the transformation of social services into digital forms, the actual research is the search for ideas, concepts, startups, pilot projects and features of the introduction of blockchain technology in education. The first Bitcoin blockchain was designed as an extremely secure and reliable information network for the financial sector, where the storage and transfer of value assets is the main functionality. In the field of education and educational processes, the issue of information security is most acute, since it is a question of reliable storage of activities of teachers and students. This issue is successfully solved by blockchain technology. The target of the work is to research engineering blockchain solutions published in academic cited publications. In order to achieve the target of the work, the most cited scientific publications over the past 5 years were collected and analyzed. The works in which conceptual and practical blockchain solutions aimed at improving efficiency, reliability and security in the operation of learning management systems (LMS) are highlighted. A new type of such blockchain systems in scientific papers is called blockchain learning management systems (BLMS). The research of some of the most well-known blockchain systems is divided into five of the following groups: certification of learning results; accreditation of educational programs; security of learning management systems; learning and educational projects; management of learning results and rewards. The publications reflect a number of resolved issues regarding course certification, digital degree graduation, e-portfolios, and reputational rewards. Many issues remain unresolved, in particular, the cost and complexity of blockchain technology, the irreversibility of actions in the blockchain, the lack of mass perception and a comfortable interface for users.

A survey of blockchain: concepts, applications and challenges

With the development of Bitcoin, organisations, be it businesses or institutions, are centring on leveraging Bitcoin's blockchain technology to nonmonetary based applications to improve efficiency of the activities. Having various benefits like anonymity, decentralised, audibility etc. Blockchain technology can be vastly implemented in various sectors other than financial too. This paper gives an overview the blockchain technology. It briefs about various technical concepts used in the blockchain, its types and where it can be used. It also discusses some proposed applications of the technology and tools or frameworks that can be used to develop such. It also presents the limitations of the technology.

Effective Return Rate Prediction of Blockchain Financial Products Using Machine Learning

In recent times, financial globalization has drastically increased in different ways to improve the quality of services with advanced resources. The successful applications of bitcoin Blockchain (BC) techniques enable the stockholders to worry about the return and risk of financial products. The stockholders focused on the prediction of return rate and risk rate of financial products. Therefore, an automatic return rate bitcoin prediction model becomes essential for BC financial products. The newly designed machine learning (ML) and deep learning (DL) approaches pave the way for return rate predictive method. This study introduces a novel Jellyfish search optimization based extreme learning machine with autoencoder (JSO-ELMAE) for return rate prediction of BC financial products. The presented JSO-ELMAE model designs a new ELMAE model for predicting the return rate of financial products. Besides, the JSO algorithm is exploited to tune the parameters related to the ELMAE model which in turn boosts the classification results. The application of JSO technique assists in optimal parameter adjustment of the ELMAE model to predict the bitcoin return rates. The experimental validation of the JSO-ELMAE model was executed and the outcomes are inspected in many aspects. The experimental values demonstrated the enhanced performance of the JSO-ELMAE model over recent state of art approaches with minimal RMSE of 0.1562.

Application of Elliptic Curve Crypto System to Secure Multi-Signature Bitcoin Block Chain

Application of Elliptic Curve Crypto System to Secure Multi-Signature Bitcoin Block Chain

An Optimal Algorithm for Secure Transactions in Bitcoin Based on Blockchain

Technological advancement has made a significant contribution to the change of the economy and the advancement of humanity. Because it is changing how economic transactions are carried out, the blockchain is one of the technical developments that has a lot of promise for this progress. The public record of the Bitcoin blockchain provides dispersed users with evidence of transaction ownership by publishing all transaction data from block reward transactions to unspent transaction outputs. Attacks on the public ledger, on the other hand, are a result of the fact that all transaction information are exposed. De-anonymization attacks allow users to link transaction entities and acquire user privacy through specified transaction amounts. As a result, in light of the Bitcoin blockchain system’s privacy issues, this scheme combines the concept of coin mixing with encrypted transaction technology to create a truly anonymous blockchain system that preserves the payer identity and transaction amount privacy. The one-way aggregated signature technique of Boneh, Gentry, and Lynn systematically embeds the notion of mixing into the whole block. The homomorphic encryption approach of Boneh, Goh, and Nissim allows miners to check the legality of encrypted transactions. Miners will validate transactions, conceal transactions, and package transactions as entities in the scheme. Finally, this technique was chosen after a comparison of several privacy-preserving blockchain schemes. It not only ensures complete anonymity, but also keeps transaction storage overhead to a minimum.

Stateful Layered Chain Model to Improve the Scalability of Bitcoin

Bitcoin becomes the focus of scientific research in the modern era. Blockchain is the underlying technology of Bitcoin because of its decentralization, transparency, trust-less, and immutability features. However, blockchain can be considered the cause of Bitcoin scalability issues, especially storage. Nodes in the Bitcoin network need to store the full blockchain to validate transactions. Over time, the blockchain size will be bulky. So, the full nodes will prefer to leave the network. This leads to the blockchain being centralized and trusted, and the security will be adversely affected. This paper proposes a Stateful Layered Chain Model based on storing accounts’ balances to reduce the Bitcoin blockchain size. This model changes the structure of the traditional blockchain from blocks to layers. The experimental results demonstrated that the proposed model reduces the blockchain size by about 50.6 %. Implicitly, the transaction throughput can also be doubled.

Attestation of Improved SimBlock Node Churn Simulation

Node churn, or the constant joining and leaving of nodes in a network, can impact the performance of a blockchain network. The difficulties of performing research on the actual blockchain network, particularly on a live decentralized global network like Bitcoin, pose challenges that good simulators can overcome. While various tools, such as NS-3 and OMNet++, are useful for simulating network behavior, SimBlock is specifically designed to simulate the complex Bitcoin blockchain network. However, the current implementation of SimBlock has limitations when replicating actual node churn activity. In this study, the SimBlock simulator was improved to simulate node churn more accurately by removing churning nodes and dropping their connections, and increasing additional instrumentation for validation. The methodology used in the study involved modeling the Bitcoin node churn behavior based on previous studies and using the enhanced SimBlock simulator to simulate node churn. Empirical studies were then conducted to determine the suitability and limitations of the node churn simulation. This study found that the improved SimBlock could produce results similar to observed indicators in a 100-node network. However, it still had limitations in replicating node churn behavior accurately. It was discovered that SimBlock limits all nodes to operate as mining nodes and that mining is simulated in a way that does not depict churn accurately at any time but only at specific intervals or under certain conditions. Despite these limitations, the study’s improvements to SimBlock and the identification of its limitations can be useful for future research on node churn in blockchain networks and the development of more effective simulation tools.

A Novel Epoch-Based Transaction Consistency Sorting Protocol for DAG Distributed Ledger

Because of the characteristics of decentralization, immutability, and transparency, blockchain has gradually become a new and revolutionary technology, which has far-reaching significance for the development of modern technology. However, the traditional Bitcoin blockchain that supports synchronous consensus suffers from the fatal flaw of low throughput. To improve throughput, a number of DAG distributed ledgers have been proposed that support asynchronous consensus, all of which allow multiple nodes to process concurrent transactions asynchronously. However, most DAG distributed ledgers do not implement consistent sorting of transactions, making it difficult to deploy smart contracts. To overcome this problem, in this paper, an epoch-based transaction consistency sorting protocol for DAG distributed ledger is proposed, which not only provides the possibility for the deployment of smart contracts but also can be used to resolve conflicting transactions in the ledger. Transaction consistency sorting protocol provides a more reasonably ordered list of all transactions by taking scalars, such as the set of their own past and future, parent block, and timestamp. In addition, through theoretical analysis, the stability and rationality of the transaction consistency sorting protocol are proved, and there is no Condorcet cycle. Finally, the simulation results demonstrate the protocol is efficient and achieve a throughput of at least 2000 transactions per second.