Bitcoin Transactions Research Articles

Sensitivity analysis for a Bitcoin simulation model

Bitcoin is a popular and widely traded cryptocurrency. The Bitcoin blockchain technology makes it easy for users to conduct pseudo-anonymous financial transactions. However, it also facilitates criminals to secrete their actual identities from law enforcement agencies. Heuristic-based address clustering is the subject regarding Bitcoin de-anonymization. But no heuristic algorithm has a known or potential error rate due to the lack of ground truth. This paper uses sensitivity analysis to validate and verify a constructed Bitcoin simulation model. The evaluation and validation processes examine the model behavior and model outputs from multiple simulation runs to demonstrate fidelity and credibility. The analysis results show no model uncertainties, and the simulation model is stable and can effectively simulate Bitcoin transactions. With a reasonable number of nodes and transaction volumes in the simulated network, the simulation model can be used to verify the effectiveness of two widely used heuristic-based address clustering algorithms and measure the corresponding error rates.

Novel blockchain transaction provenance model with graph attention mechanism

With the maturity of the blockchain technology, more and more blockchain digital currencies including the Bitcoin, the Ethereum and the Ripple have been developed. Meanwhile, the security problem of the blockchain digital currency has become increasingly serious. It is necessary to build a provenance model for a large number of blockchain transaction, which can be used to find out which link has trouble and who is responsible once a problem occurs. However, the existing blockchain transaction data analysis methods have low traceback accuracy for data provenance. Therefore, a novel blockchain transaction provenance model with graph attention mechanism is proposed in this paper. The graph attention mechanism is used to identify the traders. Then, a triplet data structure is built to record provenance information and express the relationship between transaction amount and traders. Finally, the smart contract is used to implement the proposed provenance model. The provenance information can be searched through the interface provided by the smart contract when the traceback is required. In addition, the real large-scale Bitcoin transaction dataset Elliptic is used to compare the proposed method with other existing methods. The experiment show that the proposed method achieves state of the art results of the traceback accuracy.

An Analysis of Bitcoin Zakat Calculation

Nowadays, the use of digital currency technology is recognised by a variety of industries, particularly those that use Bitcoin transactions. Bitcoin was created as a digital currency to facilitate online payment transactions between one party or individual and another without the use of a third party. However, Bitcoin is also used for money laundering, investment scheme fraud, and involvement in cybercrime, funding activities, and other illegal activities. Bitcoin is also unsuitable as a value storage tool because Bitcoin wallet virtual securities are frequently targeted by cyber criminals, and they can lose liquidity and become vulnerable to hyperinflation. This scenario compels experts and scientists from all over the world to pay close attention to hakam law and the risks of Bitcoin use. According to the Fatwa issued by the state of Perlis explains that any ownership of digital assets is subject to zakat if it is sufficient haul (storage period) and nisab (mandatory rate). This is because digital currency is property and currency that is mutaqawwim which is legal property according to sharia. The issue is whether zakat institutions are willing to accept zakat via Bitcoin transactions. How to make zakat payments with Bitcoin. This study’s objective is to dismantle the method for calculating zakat on Bitcoin currency. This study collects data qualitatively through interviews with representatives of zakat institutions and Bitcoin practitioners. While data analysis is based on document analysis and data interpretation of information obtained through interviews. The findings of this study are calculation methods that can be applied to zakat Bitcoin currency. Thus, this study is able to provide new contributions to the academic development and management of zakat in the initial preparation and organise strategies for technological change and innovation that is growing rapidly in changing the way zakat collection today.

The Privacy Limits of Transacting in Bitcoin

The Bitcoin blockchain, a prime example of disruptive technology, has fundamentally altered the way various industries approach remote transactions. Bitcoin grants privacy to its users by anonymizing public keys, provides autonomy by eliminating the need for trusted third parties, and maintains transparency through its public disclosure protocol. Bitcoin is an innovative manifestation of the Fourth Amendment ideals of security and autonomy. It is, thus, no surprise that the Bitcoin blockchain presents unprecedented Fourth Amendment challenges for courts to consider.
 In United States v. Gratkowski, the Fifth Circuit addressed the novel issue of whether Fourth Amendment protections extend to an individual’s Bitcoin transactions. Notably, the court was the first to find that an individual does not have a privacy interest in their information located directly on the Bitcoin blockchain. However, the Fifth Circuit applied inconsistent and flawed reasoning in reaching this decision, demonstrating a fundamental misunderstanding of Bitcoin and its users.
 Accordingly, this Note argues that the Gratkowski decision should be applied narrowly and with caution, especially considering the Supreme Court’s warnings against the incompatibility of current Fourth Amendment doctrine with the digital age. It then suggests implementing a modified reasonable expectation-of-privacy standard, supplementing the current standard with an additional inquiry into what information an individual disclosed when initiating a transaction. This modified standard would preserve the integrity of Bitcoin, while simultaneously articulating a proper framework for assessing privacy concerns in the context of Bitcoin and blockchain technology.

Need for speed, but how much does it cost? Unpacking the fee‐speed relationship in Bitcoin transactions

AbstractThe growing popularity of blockchain‐based cryptocurrencies is driven by the flexibility in transaction fee offerings, among other factors. To achieve service‐level differentiation among their users, many cryptocurrencies allow users to “name your own price,” giving rise to a large variation in fee offerings and hence, variation in confirmation times. Yet, the time it takes a cryptocurrency transaction to be confirmed in the blockchain is not only affected by the fee offered, but also by the contemporaneous congestion level and the inherent randomness in the verification process. Although it is generally expected that higher fees lead to quicker confirmation, the uniqueness of the cryptocurrency setting adds important nuances to the fee‐speed relationship. Using Bitcoin—the original and most heavily used cryptocurrency by far—as our empirical context, we stylize the transaction confirmation processes, propose a theoretical framework that maps the causal path from fee to speed, and estimate this framework using Bitcoin transaction data under periods of high volatility. Our results show strong evidence for two characteristics of fee's impact: congestion dependence and tail shrinkage. Our finding that the speed acceleration effect of fee is particularly strong on the tail of the confirmation time distribution motivates a target service level approach to fee recommendation. To put this finding into practice, we develop an efficient computational procedure that helps Bitcoin users accurately estimate fees based on their confirmation delay preferences. We discuss the implications of our analyses on future cryptocurrency development and the long‐term adoption of this revolutionary technology.

Graph-Based LSTM for Anti-money Laundering: Experimenting Temporal Graph Convolutional Network with Bitcoin Data

Elliptic data—one of the largest Bitcoin transaction graphs—has admitted promising results in many studies using classical supervised learning and graph convolutional network models for anti-money laundering. Despite the promising results provided by these studies, only few have considered the temporal information of this dataset, wherein the results were not very satisfactory. Moreover, there is very sparse existing literature that applies active learning to this type of blockchain dataset. In this paper, we develop a classification model that combines long-short-term memory with GCN—referred to as temporal-GCN—that classifies the illicit transactions of Elliptic data using its transaction’s features only. Subsequently, we present an active learning framework applied to the large-scale Bitcoin transaction graph dataset, unlike previous studies on this dataset. Uncertainties for active learning are obtained using Monte-Carlo dropout (MC-dropout) and Monte-Carlo based adversarial attack (MC-AA) which are Bayesian approximations. Active learning frameworks with these methods are compared using various acquisition functions that appeared in the literature. To the best of our knowledge, MC-AA method is the first time to be examined in the context of active learning. Our main finding is that temporal-GCN model has attained significant success in comparison to the previous studies with the same experimental settings on the same dataset. Moreover, we evaluate the performance of the provided acquisition functions using MC-AA and MC-dropout and compare the result against the baseline random sampling model.

Bitcoin address clustering method based on multiple heuristic conditions

AbstractSingle heuristic method and incomplete heuristic conditions were difficult to cluster a large number of addresses comprehensively and accurately. Therefore, this paper analysed the associations between Bitcoin transactions and addresses and used six heuristic conditions to cluster addresses and entities. We proposed an improved change address detection algorithm and compared it with the original change address algorithm to prove the effectiveness of the improved algorithm. By adding conditional constraints, the identified change address was more accurate, and the convergence speed of the algorithm was accelerated. Our work presented the pseudo‐anonymity mechanism of the Bitcoin system, which could be used by the law enforcement agencies to track and crack down illegal transactions.

Can Bitcoin be Trusted? Quantifying the economic value of blockchain transactions

Bitcoin is the largest blockchain, and provides the underlying UTXO architecture used by many other cryptocurrencies. We identify an inherent bias embedded in this architecture (the Notebreaker mechanism) which forces users to ‘spend’ the entire content of a wallet address in order to make a payment, receiving ‘change’ into a unique new address. This inflates both the apparent volume transacted and network users, as well as minimizing the apparent fees of transacting. We develop an innovative Transaction Identification Methodology (TIM) to quantify the economic value of transactions from raw blockchain data. Using four different algorithms across three stages, we achieve 95% accuracy in quantifying the degree of bias in these measures. Validated across more than 430 million Bitcoin transactions involving 600 million wallet addresses, our methodology reveals that the Notebreaker mechanism inflates transaction volumes 8 times, makes the actual costs of blockchain transactions appear 3–7 times more expensive than what is commonly reported, and inflates wallet counts – a common heuristic of unique adopter counts. We provide a remediation strategy to make Bitcoin blockchain data a more accurate representation of reality, and provide a daily data set of these remediated volumes and transaction fees.

Anonymous Bitcoin transaction: A solution using secure multiparty computation

AbstractBitcoin is a popular form of cryptocurrencies. Bitcoin provides users' anonymity through cryptographic pseudonyms. Bitcoin operates on a peer‐to‐peer network that maintains a public ledger, calledblockchain, to log all transactions from one pseudonym to other, thereby hides the identity of the users. A transaction graph from theblockchainmay unveil certain users' identities. To sustain with users' anonymity, mixing is often applied.CoinJoin,MixCoin,CoinShuffle,CoinPartyandSecureCoinare some of the Bitcoin protocols that applymixnet.Mixnethas certain limitations. Firstly, protocols assume “escrow addressee” that collects the coins, and performsmixing. The “escrow addressee” must be trustworthy. Secondly, protocols which do not assume “escrow addressee,” often usemixnet.Mixnetrequires every participant must sign all others transactions. This incurs large volume of multi‐signature. Presently, Bitcoin protocol can include at most 15 multi‐signatures. Therefore, the protocols are not scalable. Finally,mixnetis “all‐or‐nothing.” That is, if all mixnodes are active then only the output is guaranteed. We present a multiparty shuffling protocol to anonymize the Bitcoin transactions. Our protocol is free from “escrow addressee” and multi‐signature. The protocol executes in multiple rounds. The protocol is not “all‐or‐nothing.” That means, every round guarantees some degree of anonymity.

Deciphering Bitcoin Blockchain Data by Cohort Analysis

Bitcoin is a peer-to-peer electronic payment system that has rapidly grown in popularity in recent years. Usually, the complete history of Bitcoin blockchain data must be queried to acquire variables with economic meaning. This task has recently become increasingly difficult, as there are over 1.6 billion historical transactions on the Bitcoin blockchain. It is thus important to query Bitcoin transaction data in a way that is more efficient and provides economic insights. We apply cohort analysis that interprets Bitcoin blockchain data using methods developed for population data in the social sciences. Specifically, we query and process the Bitcoin transaction input and output data within each daily cohort. This enables us to create datasets and visualizations for some key Bitcoin transaction indicators, including the daily lifespan distributions of spent transaction output (STXO) and the daily age distributions of the cumulative unspent transaction output (UTXO). We provide a computationally feasible approach for characterizing Bitcoin transactions that paves the way for future economic studies of Bitcoin.

Emergence and structure of decentralised trade networks around dark web marketplaces

Dark web marketplaces (DWMs) are online platforms that facilitate illicit trade among millions of users generating billions of dollars in annual revenue. Recently, two interview-based studies have suggested that DWMs may also promote the emergence of direct user-to-user (U2U) trading relationships. Here, we carefully investigate and quantify the scale of U2U trading around DWMs by analysing 31 million Bitcoin transactions among users of 40 DWMs between June 2011 and Jan 2021. We find that half of the DWM users trade through U2U pairs generating a total trading volume greater than DWMs themselves. We then show that hundreds of thousands of DWM users form stable trading pairs that are persistent over time. Users in such stable pairs turn out to be the ones with the largest trading volume on DWMs. Then, we show that new U2U pairs often form while both users are active on the same DWM, suggesting the marketplace may serve as a catalyst for new direct trading relationships. Finally, we reveal that stable U2U pairs tend to survive DWM closures and that they were not affected by COVID-19, indicating that their trading activity is resilient to external shocks. Our work unveils sophisticated patterns of trade emerging in the dark web and highlights the importance of investigating user behaviour beyond the immediate buyer-seller network on a single marketplace.

Complex Network Analysis of the Bitcoin Transaction Network

In this brief, we conduct a complex-network analysis of the Bitcoin transaction network. In particular, we design a new sampling method, namely random walk with flying-back (RWFB), to conduct effective data sampling. We then conduct a comprehensive analysis of the Bitcoin network in terms of the degree distribution, clustering coefficient, the shortest-path length, connected component, centrality, assortativity, and the rich-club coefficient. We obtain several important observations including the small-world phenomenon, multi-center status, preferential attachment, and non-rich-club effect of the current network. This work brings up an in-depth understanding of the current Bitcoin blockchain network and offers implications for future directions in malicious activity and fraud detection in cryptocurrency blockchain networks.

Towards bitcoin transaction anonymity with recurrent attack prevention

Towards bitcoin transaction anonymity with recurrent attack prevention

Multi-player dynamic game model for Bitcoin transaction bidding prediction

With the rapid rise of cryptocurrencies, it has become an urgent problem to realize the flat use of digital currency, with making it really put into use, and giving full play to its utility in the current economic market. This paper innovatively takes the maximization of user benefit as the key point to predict transaction bidding price combining dynamic game theory. The bidding price of user transaction not only refers to historical transactions, but also considers the impact on future subsequences, and the result describes the interaction between transactions in detail. Also this paper proposes a method to express user satisfaction and establishes a user benefit model accordingly, so as to ensure the transaction is packaged successfully to the greatest extent within the acceptable range of transaction pricing. Finally this paper compares the proposed model with conventional machine learning prediction algorithms, finding that when user does not participate in the trading for the first time, the prediction effect of this proposal is better than that of machine learning over small data sets, moreover superior to machine learning methods in prediction accuracy and sensitivity, with a lower time complexity.

Bitcoin Ransomware Detection Employing Rule-Based Algorithms


 Cryptocurrencies have completely altered the digital transaction process all over the globe. Almost a decade after Satoshi Nakamoto generated the first Bitcoin block; many cryptocurrencies have been established. The Ransomware attack is a type of cybercrime and a class of malware that encrypts the files and prevents users from accessing their data or systems and demands payment for decrypting and retrieving access to their files. Ransomware data classification using present data mining and machine learning methods is difficult because predictions aren't always correct. We aim to build two models that effectively address these challenges and can diagnose and classify Ransomware attacks accurately, then compare the performance of the models. In this paper, we investigated the use of Rule-Based algorithms for mining Bitcoin Ransomware Data to classify Ransomware attacks in Bitcoin transactions. Employing Rule-Based techniques in detecting Bitcoin data is beneficial because the algorithms effectively classify non-linear datasets. The analysis was done on a Bitcoin dataset for 61,004 addresses selected from 29 Ransomware families and contained ten descriptive and decision attributes. Both Rule-Based algorithms were illustrated and compared on the dataset employing 10-fold cross-validation. Experimental results show that classification under partial decision tree (PART) algorithm performed better in different metrics than the Decision Table algorithm. It provides an accuracy of 96.01%, a recall of 96%, a precision of 95.9%, and an F-Measure of 95.6%. Experimental results propose that it is beneficial to further investigate the application of PART to predictive modelling tasks in Ransomware studies.

Can Lightning Network’s Autopilot Function Use BA Model as the Underlying Network?

By extending micropayment channel technology and building a transaction network, the Lightning Network solves inefficient bitcoin transactions. Currently, more than 1,000 Bitcoins have been deposited in the Lightning Network. In designing the Lightning Network routing protocol, simulating its transactions, and evaluating the network robustness, researchers have almost always used the Barabasi Albert Model as a substrate network. In particular, as the network grows in size, it becomes particularly important to automatically establish links for the network of joined nodes—the autopilot function—and it becomes a crucial question whether the Barabasi Albert Model as the underlying network for the autopilot function conforms to the real topology of the Lightning Network. In this paper, we construct the temporal network of Lightning Network and compare the topological properties of Lightning Network with those of Barabasi Albert Model of the same scale in detail. Lightning Network has a large gap with Barabasi Albert Model in terms of assortativity and network diameter. We found that nodes tend to connect to nodes with greater Closeness Centrality in terms of node preference connectivity. Our findings suggest that using the Barabasi Albert Model as the underlying network for the autopilot function is not a reasonable choice.

A Systematic Review of Detecting Illicit Bitcoin Transactions

In recent years, the application of virtual currency has become a part of people's life. The decentralization and anonymity of Bitcoin have made it a favorite tool for many criminals. Therefore, how to trace illegal activities in Bitcoin transactions has become one of the most important research areas. This paper systematically collects 25 research results in this field since 2018, and divides them into three areas, i.e., supervised learning, unsupervised learning, and topological analysis. The supervised learning method based on machine learning is the current mainstream in this research field. However, we believe that the model can achieve more accurate results after combining unsupervised learning and topological analysis features. Moreover, topology analysis can help to observe the entire or specific part of the Bitcoin trading network from a macro perspective so as to discover the hidden illegal activities. In addition, data visualization techniques can provide structural insights to understand the Bitcoin trading network.

Get off of Chain: Unveiling Dark Web Using Multilayer Bitcoin Address Clustering

Bitcoin is the most widely used cryptocurrency for illegal trade in current darknet markets. Owing to the anonymity of its addresses, even though transaction flows are globally visible, Bitcoin clustering remains one of the most challenging and open problems in illegal Bitcoin transaction analysis. In this article, to resolve this problem, we propose a novel <i>multi-layer heuristic</i> algorithm for Bitcoin clustering, which leverages on-chain transactions as well as off-chain application data in the real world. For this purpose, we first explored the unique characteristics of darknet market ecosystems including their trading systems. By conducting an in-depth analysis of the data manually collected for 11 months, we found that some darknet market review data disclosed transactions containing Bitcoin value and item delivery information. We then identified unique Bitcoin addresses associated with the disclosed information, owned by the same darknet providers. Based on address ownership, more accurate market clusters could be created, which have not previously been identified by other clustering algorithms. According to our experimental results, approximately 31.68&#x0025; of the darknet market review data matched real Bitcoin transactions, and 122 hidden clusters associated with Silk Road 4 were found. This indicates that the proposed algorithm can complement existing clustering methods and significantly reduce the false negative rate by up to 91.7&#x0025;.

A Survey on Technologies Which Make Bitcoin Greener or More Justified

According to recent estimates, one bitcoin transaction consumes as much energy as 1.5 million Visa transactions. Why is bitcoin using so much energy? Most of the energy is used during the bitcoin mining process, which serves at least two significant purposes: a) distributing new cryptocurrency coins to the cryptoeconomy and b) securing the Bitcoin blockchain ledger. In reality, the comparison of bitcoin transactions to Visa transactions is not that simple. The amount of transactions in the Bitcoin network is not directly connected to the amount of bitcoin mining power nor the energy consumption of those mining devices; for example, it is possible to multiply the number of bitcoin transactions per second without increasing the mining power and the energy consumption. Bitcoin is not only &#x201C;digital money for hackers&#x201D;. It has very promising future potential as a global reserve currency and a method to make the World Wide Web (WWW) immune to cyberattacks such as the Distributed Denial-of-Service attacks. This survey approached cryptocurrencies&#x2019; various technological and environmental issues from many different perspectives. To make various cryptocurrencies, including bitcoin (BTC) and ether (ETH), greener and more justified, what technological solutions do we have? We found that cryptocurrency mining might be cleaner than is generally expected. There is also a plan to make a vast renewable energy source available by combining Ocean Thermal Energy Conversion and Bitcoin mining. There are plans to use unconventional computing methods (quantum computing, reversible computing, ternary computing, optical computing, analog computing) to solve some of the issues regarding the vast energy consumption of conventional computing (including cryptocurrency mining). We think using spare computing cycles for grid computing efforts is justified. For example, there are billions of smartphones in the world. Many smartphones are being recharged every day. If this daily recharging period of twenty to sixty minutes would be used for grid computing, for example, finding new cures to cancer, it would probably be a significant breakthrough for medical research simulations. We call cryptocurrency communities to research and develop grid computing and unconventional computing methods for the most significant cryptocurrencies: bitcoin (BTC) and ether (ETH).

РАЗРАБОТКА САМОИСПОЛНЯЮЩИХСЯ СМАРТ-КОНТРАКТОВ НА ETHEREUM ДЛЯ КОНТЕЙНЕРНЫХ ПЕРЕВОЗОК В КИТАЕ

Blockchain is a revolutionary new technology that facilitates peer-to-peer transfer of value without the need for a centralised intermediary. It uses cryptographic hashing functions, consensus protocols and decentralised data storage to ensure security, decentralised trust, immutability and transparency of business transactions. Blockchain technology paired with smart contracts is a reliable method of automating business rules in an efficient and trustworthy mechanism. A smart contract is a self-executing piece of code that runs on a blockchain platform. Predefined rules between participating organisations are translated into smart contract functions to establish trust. Blockchain is undergoing rapid development and has revolutionary potential for application in Intelligent Transport Systems applications. Blockchain can be used to create a secure, reliable and decentralised autonomous system, creating the best use of legacy infrastructure and resources. One of the most important issues is security, caused by the evolution of ITS towards centralisation. Fast-growing technologies, including the Internet of Things (IoT) and cloud computing, allow most data processing, analysis and decision-making to be done by centralised systems. The distinguishing factor of Ethereum is that it is programmed according to user requirements, rather than providing users with a set of predefined transactions, such as Bitcoin transactions. As a result, it could theoretically be used to automatically and securely execute any transaction involving trust, security or persistence. Although Ethereum is a relatively new implementation of blockchain, it has sparked reaction because of its potential to change the way many businesses operate.