Bitcoin Blockchain Research Articles

Influencer detection meets network autoregression — Influential regions in the bitcoin blockchain

Known as an active global virtual money network, the Bitcoin blockchain, with millions of accounts, has played a continually increasingly important role in fund transition, digital payment, and hedging. We propose a method to Detect Influencers in Network AutoRegressive models (DINAR) via sparse-group regularization to detect regions influencing others across borders. For a granular analysis, we analyse whether the transaction size plays a role in the dynamics of the cross-border transactions in the network. With two-layer sparsity, DINAR enables discovering (1) the active regions with influential impact on the global digital money network and (2) whether changes in the size of the transaction affect the dynamic evolution of Bitcoin transactions. In the analysis of real data of the Bitcoin blockchain from Feb 2012 to December 2021, we find that influence from certain regions is linked to the economic need to use BTC, such as to circumvent sanctions, avoid high inflation, and to carry out transactions through off-shore markets. The effects are robust to different groupings, evaluation periods, and choices of regularization parameters.

Mapping Distributed Ledger Technology Characteristics to Use Cases in Healthcare: A Structured Literature Review

Following the success of the Bitcoin blockchain, distributed ledger technology (DLT) has received extensive attention in health informatics research. Yet, the healthcare industry is highly complex with many different stakeholders, information systems, regulations, and challenges. Thus, DLT may be used in various settings and for different purposes. First surveys have started to synthesize our knowledge of the different use cases, in which healthcare may benefit from DLT implementations. However, an in-depth understanding of whether and how these use cases differ concerning their requirements of DLT characteristics (i.e., technical or administrative design features) is still lacking. In this work, we conducted a structured review of 185 studies on DLT-based applications in healthcare. The results reveal six pertinent use cases, each with its own combination of different purposes that DLT is used for. Furthermore, our study shows that each of these use cases has a unique set of requirements with regard to the most important DLT characteristics. In doing so, we seek to guide practitioners in the development of highly effective DLT-based applications in various healthcare settings and pave the way for future research to investigate the understudied areas of DLT-based applications in healthcare.

Does a higher hashrate strengthen Bitcoin network security?

In the blockchain world, proof-of-work is the dominant protocol mechanism that determines the consensus of the ledger. The hashrate, a measure of the computational power directed toward securing a blockchain through proof-of-work consensus, is a fundamental measure of preventing various attacks. This study tests the causal relationship between the hashrate and the security outcome of the Bitcoin blockchain. We use vector error correction modeling to analyze the endogenous relationships between the hashrate, Bitcoin price, and transaction fee, revealing the need for an additional variable to achieve our aim. Employing a measure summarizing the growth of demand factors in the Bitcoin ecosystem indicates that hashrate fluctuations significantly influence security level changes. This result underscores the importance of the hashrate in ensuring the security of the Bitcoin blockchain.

Security of the Secp256k1 Elliptic Curve used in the Bitcoin Blockchain

The article delves into the intricate characteristics and security properties of the secp256k1 elliptic curve used for the generation of addresses in the Bitcoin blockchain. The Bitcoin blockchain is a decentralized digital ledger that records all transactions made with Bitcoin cryptocurrency. In this work, the secp256k1 elliptic curve and its parameters and the method of generating private and public keys using random numbers are described. While the private key allows for the signing of transactions to spend Bitcoin, the corresponding public key and address enable others to verify transactions and send funds to that specific address on the blockchain, ensuring security, authenticity, and privacy in the decentralized network. The attacks on the use of secp256k1 for generating the bitcoin addresses like the Brute force attack, twist attack, fault attacks, and side channel attacks in the implementation of the elliptic curve are discussed. By maintaining the security and integrity of secp256k1, we can ensure that cryptographic operations, such as digital signatures and key exchanges, remain uncompromised. If the curve's security were compromised, malicious users could potentially derive private keys from public keys, leading to unauthorized transactions, double-spending, or other malicious activities. The security of implementation can be enhanced by ensuring cryptographic libraries and software implementations that utilize secp256k1 undergo thorough testing and validation to ensure correct and secure operations. The important attacks on blockchain technology like the 51% attack, Sybil attack, Double Spending attack, and Smart Contract vulnerabilities are discussed. Through a comprehensive exploration, readers will gain insights into why this particular elliptic curve was chosen for use in Bitcoin's cryptographic protocols, highlighting its role in ensuring the robustness and integrity of the blockchain ecosystem.

Adaptive Double-Spending Attacks on PoW-Based Blockchains

Previous state-of-the-art studies have proposed various analytical models to understand the double-spending attacks (DSA) occurred in Proof-of-Work (PoW) based Blockchains. Although many insights behind the double-spending attacks have been disclosed, we still believe that advanced versions of DSA can be developed to create new threats for the PoW-based blockchains such as the Bitcoin blockchain. In this paper, we present two new types of double-spending attacks in the context of the PoW-based blockchain and discuss the insights behind them. By considering more practical network parameters, such as the number of confirmation blocks, the hashpower of the double-spending attacker, the amount of coins in the target transaction, and the network-status parameter, we first analyze the success probability of the conventional double-spending attack, named Naive DSA. Based on Naive DSA, we create two new adaptive DSA, i.e., the Adaptive DSA and the Reinforcement Adaptive DSA (RA-DSA). In our analytical models, a double-spending attack is converted into a Markov Decision Process. We then exploit the Stochastic Dynamic Programming (SDP) approach to obtain the optimal attack strategies under Adaptive DSA and RA-DSA. Numerical simulation results demonstrate the correlations between each critical network parameter and the expected attacker's reward. Through the proposed analytical models, we aim to alert the PoW-based blockchain ecosystem that the threat of double-spending attacks is still at a dangerous level. For example, our findings show that the attacker can launch a successful attack with a small hashpower proportion much lower than 51% under RA-DSA.

Bitcoin as a Transaction Ledger: A Composable Treatment

Bitcoin is one of the most prominent examples of a distributed cryptographic protocol that is extensively used in reality. Nonetheless, existing security proofs are property-based, and as such they do not support composition. In this work, we put forth a universally composable treatment of the Bitcoin protocol. We specify the goal that Bitcoin aims to achieve as an instance of a parameterizable ledger functionality and present a UC abstraction of the Bitcoin blockchain protocol. Our ideal functionality is weaker than the first proposed candidate by Kiayias, Zhou, and Zikas [EUROCRYPT’16], but unlike the latter suggestion, which is arguably not implementable by the UC Bitcoin protocol, we prove that the one proposed here is securely UC-realized by the protocol assuming access to a global clock, to model time-based executions, a random oracle, to model hash functions, and an idealized network, to model message dissemination. We further show how known property-based approaches can be cast as special instances of our treatment and how their underlying assumptions can be cast in UC as part of the setup functionalities and without restricting the environment or the adversary.

Bitcoin Ordinals: Determinants and impact on total transaction fees

Bitcoin Ordinals are on-chain non-fungible assets native to the Bitcoin blockchain. As the data is stored on-chain, by opposition to Ethereum-like NFTs, creating an ordinal (i.e. “inscribing” it) can be expensive. We analyze the initial wave of Ordinals from December 14th 2022 until April 16th 2023. We show that ordinal inscriptions tend to have a lower fee rate than regular transactions. We also show that a high average block transaction fee rate is associated with lower ordinal inscriptions. These effects are larger for large inscriptions. This suggests that ordinal users are careful when setting the fee rate of their inscriptions. We also show that both the transaction fee rate for non-inscription transactions and total block fees have increased with the popularity of Bitcoin Ordinals, increasing miners’ revenue.

Segmenting Bitcoin Transactions for Price Movement Prediction

Cryptocurrencies like Bitcoin have received substantial attention from financial exchanges. Unfortunately, arbitrage-based financial market price prediction models are ineffective for cryptocurrencies. In this paper, we utilize standard machine learning models and publicly available transaction data in blocks to predict the direction of Bitcoin price movement. We illustrate our methodology using data we merged from the Bitcoin blockchain and various online sources. This gave us the Bitcoin transaction history (block IDs, block timestamps, transaction IDs, senders’ addresses, receivers’ addresses, transaction amounts), as well as the market exchange price, for the period from 13 September 2011 to 5 May 2017. We show that segmenting publicly available transactions based on investor typology helps achieve higher prediction accuracy compared to the existing Bitcoin price movement prediction models in the literature. This transaction segmentation highlights the role of investor types in impacting financial markets. Managerially, the segmentation of financial transactions helps us understand the role of financial and cryptocurrency market participants in asset price movements. These findings provide further implications for risk management, financial regulation, and investment strategies in this new era of digital currencies.

An Exploration of the Money Laundering Associated with the Bitfinex Bitcoin Hack

ABSTRACT Bitcoin, launched in 2009, has gone through inconspicuous years, halcyon years, and now, the chaos years. Writers predicted that bitcoin would replace fiat currencies and the underlying technology, blockchain, would significantly reduce the need for auditors. However, there have been numerous major cryptocurrency crimes and related bankruptcies. Called the “heist of the century,” in the 2016 Bitfinex hack, 119,754 bitcoins were illegally transferred from over 2,000 Bitfinex accounts to an external wallet. Starting in January 2017, about 25,118 of those bitcoins were transferred to other exchanges. Several money laundering techniques were employed; however, the perpetrators made mistakes and were arrested in 2022. They were identified through analyzing the bitcoin blockchain, geolocating the IP addresses used for communications, and identifying individuals when bitcoin was used for “real world” transactions. The bitcoin blockchain is publicly available and would be a great resource for Big Data, data analytics, and forensics classes. JEL Classifications: K42.

A hybrid forecasting framework based on MCS and machine learning for higher dimensional and unbalanced systems

Forecasting methods and theories have been widely researched and applied in complex systems and fields such as statistical physics, econophysics, material crystals, etc. However, challenges persist in applying these methods to complex systems characterized by high dimensionality, data imbalance, and single prediction evaluation. To address these issues, we propose a novel hybrid forecasting approach that integrates the model confidence set (MCS) with machine learning (ML) models. We introduce Principal Component Analysis (PCA) to reduce dimensionality of the data, reduce data imbalance through a combination of random undersampling and oversampling, and introduce several metrics to evaluate the machine learning model set. We also introduce the MCS to select the optimal model from the set of ML models and propose a new combinatorial approach, the MCS-ML combinatorial model. An empirical study is conducted using the example of abnormal transactions in the Bitcoin blockchain. The empirical results show that the proposed MCS-ML combinatorial model has better predictive performance than the models in the ML model set under different data structures.

Analysis of concrete architecture of Bitcoin Core

Bitcoin Core serves as the foundational software responsible for verifying and validating all transactions and blocks within the Bitcoin blockchain, hence upholding the security and integrity of the whole Bitcoin network. This study extensively examines the internal mechanisms, structure, and conceptual framework of Bitcoin Core. This study aims to conduct a comprehensive analysis of the Bitcoin Core architecture, with a focus on evaluating its capacity to fulfill the rigorous demands of a decentralized Bitcoin network. This study not only provides a comprehensive understanding of the essential elements that drive the Bitcoin network, but it also explores the numerous factors that have a substantial impact on the operational availability of Bitcoin nodes. All of these factors are essential for ensuring the efficient operation of the Bitcoin network, encompassing the physical environment, architectural designs of nodes, and maintenance requirements. This study provides a comprehensive analysis of the underlying mechanisms of Bitcoin, shedding light on its inherent robustness and the factors that contribute to its consistent performance within the ever-changing landscape of digital currencies. The objective of this endeavor is to furnish a detailed exposition of the mechanics of Bitcoin, so enhancing its prevalence and familiarity among individuals.

Bitcoin Layer Two Scaling Solutions: Lightening Payment Channels Network Comprehensive Review, Mechanisms, Challenges, Open Issues and Future Research Directions

Blockchain technology era is triggered due to current advancement in the decentralized paradigms via facilitating secure collaboration among untrusted entities consequently superseding the necessity for the trusted third parties’ existence. Notwithstanding its potential, blockchain scalability faces severe limitations such as low throughput, high fees, and confirmation time latency. Several scaling solutions have been proposed, including layer one solutions that substantially require fundamentally amending blockchain underlying infrastructure resulted in further scalability complications. Layer two solutions, specifically Lightning Network, is a successful cryptographic layer built atop of the Bitcoin blockchain that aims to alleviate these implications by deporting transaction processing outside blockchain while ensuring security and consensus inherited from blockchain without compromising its infrastructure. Payment channels form the core of the lightning network, facilitating fast and secure transactions between participants. However, the network encounters substantial obstacles associated with transaction amounts that exceed the current channel capacity, leading to payment failures and limiting its effectiveness. Payment Channel Networks (PCN) have been risen as an evolved replacement that solves payment channel’s issues, seeking to enhance transaction throughput and reduce confirmation delays by solving limited capacities shortages. This paper thoroughly examines PCN efficiency impacting factors that hinder its current adoption growth and avoiding its widespread employment. Additionally, existing solutions are reviewed and categorized based on the PCN building architecture. The research concludes by outlining potential research avenues and future directions to improve the efficiency and practicality of the Lightning Network PCN. This research contribution aids the advent of blockchain layer two technology and participates in its integration into real-world applications.

A sustainable Bitcoin blockchain network through introducing dynamic block size adjustment using predictive analytics

Bitcoin is the largest cryptocurrency in the market, which uses blockchain technology to bring in features like decentralization, anonymity, and trust. However, it still struggles with broader adaptation due to long verification times and high transaction fees. As a result, it is lagging behind competitors. We need to provide faster confirmations to tackle these issues while ensuring stable earnings for the miners. However, it is challenging to increase the block sizes or decrease the average block creation time without affecting the stability and security of the network. To address this conundrum, firstly, an optimization problem is formulated where the objective is to increase the transaction count in every cycle. Based on that, a comprehensive learning framework is developed to solve the formulated problem since the issue is intractable and hard to solve in polynomial time. The proposed learning framework includes (i) implementing a viable data-driven infrastructure with a machine learning (ML) root, (ii) training learning models with efficient generalization capability, and (iii) predicting the ideal block size in every block generation cycle. Our concept uses extreme gradient boost (XGB) as its core algorithm, which analyzes nine attributes associated with the Bitcoin network. These network-allied data points assist the model in creating an adaptive block size in the blockchain. XGB, trained using the last four years of real-world data, can predict block sizes with a 63.41% accuracy. The model ensures an all-around positive change in Bitcoin with a 12.29% increase in block size, a 13.45% increase in transaction fee (USD), and a 14.88% increase in transaction approval rate and transaction count, thus addressing the long wait time and broader adaption issue.

Enhancing bitcoin transaction confirmation prediction: a hybrid model combining neural networks and XGBoost

With Bitcoin being universally recognized as the most popular cryptocurrency, more Bitcoin transactions are expected to be populated to the Bitcoin blockchain system. As a result, many transactions can encounter different confirmation delays. Concerned about this, it becomes vital to help a user understand (if possible) how long it may take for a transaction to be confirmed in the Bitcoin blockchain. In this work, we address the issue of predicting confirmation time within a block interval rather than pinpointing a specific timestamp. After dividing the future into a set of block intervals (i.e., classes), the prediction of a transaction’s confirmation is treated as a classification problem. To solve it, we propose a framework, Hybrid Confirmation Time Estimation Network (Hybrid-CTEN), based on neural networks and XGBoost to predict transaction confirmation time in the Bitcoin blockchain system using three different sources of information: historical transactions in the blockchain, unconfirmed transactions in the mempool, as well as the estimated transaction itself. Finally, experiments on real-world blockchain data demonstrate that, other than XGBoost excelling in the binary classification case (to predict whether a transaction will be confirmed in the next generated block), our proposed framework Hybrid-CTEN outperforms state-of-the-art methods on precision, recall and f1-score on all the multiclass classification cases (4-class, 6-class and 8-class) to predict in which future block interval a transaction will be confirmed.

Analyzing the peeling chain patterns on the Bitcoin blockchain

Bitcoin is a widely used decentralized cryptocurrency. The proportion of Bitcoin transactions used for illegal activities is increasing. Mixing services are commonly applied to enhance anonymity and make transaction records more challenging to follow and analyze. The current research on peeling chains is generally based on heuristic algorithms to identify change addresses. However, due to the characteristics and limitations of the Bitcoin blockchain, there is no such ground truth to ensure the accuracy of each derived change address. This research analyzes the peeling chain patterns based on self-change addresses. The use of self-change addresses implies that the input address and the address used for receiving the change are controlled by the same entity. Also, each chain's transaction details and generated chain parameters are further verified for more precise results. Combining the two methods ensures the accuracy of the extracted peeling chains to some extent. And the corresponding behavior pattern of the extracted chains is studied.

Investigating SHA and Proposing SPHINCS+ as a Post Quantum Algorithm (PQC)

Abstract: In the swiftly evolving landscape of cryptography, the advent of quantum computing poses unprecedented challenges to the established security paradigms. This research embarks on an extensive exploration into the resilience of the SHA-256 hashing algorithm, a linchpin of contemporary cryptographic infrastructure, against the looming threat posed by quantum computers. Our principal aim is to comprehensively assess the susceptibility of SHA-256, especially within the context of its critical role in ensuring the security and immutability of the Bitcoin blockchain. As quantum computing's potential to break classical cryptographic systems becomes a tangible concern, this research proposes SPHINCS+ as a potent post-quantum alternative, capable of safeguarding digital transactions and communications in the quantum era. By delving deep into the inner workings of both SHA-256 and SPHINCS+, this research contributes significantly to the expanding knowledge base surrounding post-quantum cryptography and its implications for securing the digital landscape

Fingerprinting Bitcoin entities using money flow representation learning

Deanonymization is one of the major research challenges in the Bitcoin blockchain, as entities are pseudonymous and cannot be identified from the on-chain data. Various approaches exist to identify multiple addresses of the same entity, i.e., address clustering. But it is known that these approaches tend to find several clusters for the same actor. In this work, we propose to assign a fingerprint to entities based on the dynamic graph of the taint flow of money originating from them, with the idea that we could identify multiple clusters of addresses belonging to the same entity as having similar fingerprints. We experiment with different configurations to generate substructure patterns from taint flows before embedding them using representation learning models. To evaluate our method, we train classification models to identify entities from their fingerprints. Experiments show that our approach can accurately classify entities on three datasets. We compare different fingerprint strategies and show that including the temporality of transactions improves classification accuracy and that following the flow for too long impairs performance. Our work demonstrates that out-flow fingerprinting is a valid approach for recognizing multiple clusters of the same entity.

A Review of the Lightning Network’s Evolution: Unraveling Its Present State and the Emergence of Disruptive Digital Business Models

The Lightning Network (LN), a second-layer protocol built on top of the Bitcoin blockchain, is an innovative digital payment solution that offers increased convenience, speed, and cost-effectiveness to consumers and businesses alike. However, there is limited literature available on the characteristics of this nascent technology, the depth and breadth of the various business LN-related applications as well as relevant adoption/implementation challenges. This study aims to contribute to the understanding of the LN’s characteristics, its potential in enhancing business operations and its applicability across different sectors, while taking into account adoption and implementation challenges. We apply a narrative review methodology using a semi-systematic approach to examine new and emerging business models empowered by the LN and its characteristics, topology, performance, privacy and security. We analyze the data to identify key themes and trends in the literature, offering a critical analysis of the strengths and weaknesses of the existing literature. Based on the findings, we provide several clusters of fruitful areas for future research directions. This study not only provides crucial insights for businesses contemplating the adoption of LN to improve their operations and customer experience, but it also represents a substantial academic contribution, offering valuable knowledge and fostering further research in the fields of blockchain technology, FinTech and cryptocurrencies.

Analysis of Consensus Mechanisms: PoW and PoS

Along with the trend of Bitcoin blockchain, the concept behind all virtual currency has be-come popular in the study of the Internet. This essay mainly researches two kinds of common consensus mechanisms for the current blockchain network and looks forward to the future development of the technologys usage in daily life. This research aims to overview the two most common consensus mechanisms in the construction of blockchain. By reviewing re-sources from other research, an explanation of the goal of the consensus method, the ad-vantages, and disadvantages of each approach and the future development of these two meth-ods are summarized and developed. The result of the review explains the shifts of mature vir-tual currencies from proof of work to proof of stake and advises what mechanism should be used at starting stage and why a shift is necessary for proof of stake currencies.

Bitcoin under the microscope

This paper explores and describes historical on-chain transaction data recorded on the Bitcoin blockchain, constructs a panel of all individual Bitcoin users, and computes their balances in the cross-section and over time. We run clustering algorithms to combine addresses that belong to the same user into wallets and we find that using wallets over addresses as the unit of analysis allows for more economically meaningful interpretations of user behavior. We identify and divide wallets into user categories – miners, exchanges, services, retail wallets and receiving-only addresses – and observe varying activity levels and balances in the cross-section and over time, corresponding to their intended role in the Bitcoin network. Our findings also suggest heterogeneity in financial performance across user categories with miners exhibiting higher realized returns relative to exchanges and retail users.