Blockchain technology, has the characteristics of decentralization, openness and transparency, so that everyone can participate in database recording. Therefore, blockchain technology has a good application prospect in various industries. As the most successful application of blockchain technology, the Bitcoin system applies the Proof of Work (PoW) consensus mechanism. Under the PoW consensus mechanism, each miner competes through his own power to solve a SHA256 mathematical problem together, so as to gain profits. Due to the difficulty of the cryptography puzzle, miners tend to join the mining pool to obtain stable income. And the block withholding attacks will be carried out between the mining pools, so as to maximize his own income by controlling the infiltration rate dispatched to other mining pools. In this paper, we build a game model between mining pools based on the PoW consensus algorithm, and analyze its Nash equilibrium from two perspectives. The influence of the mining pools' power, the ratio of the power to be infiltrated, and the betrayed rate of dispatched miners on the mining pool's infiltration rate selection and income were explored, and the results were obtained through numerical simulations.
In spite of the increasing popularity of Ethereum, market analysis of the corresponding cryptocurrencies Ether is relatively unexplored until now. This paper is devoted to filling in the research gap of Ether market analysis, the purpose being to provide useful insights on Ether investment. In particular, we first employ the detrended fluctuation analysis and the asymmetric multifractal detrended fluctuation analysis to investigate the properties of long-range dependence, multifractality, and its asymmetry. After that, we study the causality between returns and volume of Ether to find how the activity of investors influences returns based on a nonparametric causality-in-quantiles test. Besides, by making a comparison with the Bitcoin market, we further uncover some unique properties of the Ether market.
Cryptocurrencies including Bitcoin are known to be vulnerable to so-called ‘double-spending’ attacks, where the same digital currency is used to execute multiple different transactions simultaneously. Little is known, however, about the underlying reasons for this vulnerability. Here we develop an agent-based model to study how features of cryptocurrency networks contribute to their vulnerability to double-spending attacks. Perhaps surprisingly, we find neither the number of network nodes nor its path length seem to influence the probability of successful attacks. We find robust evidence that the network's clustering coefficient has substantial influence. In particular, scale-free networks, with their small clustering coefficients, are more than twice as likely to succumb to double-spending attacks than are networks with larger coefficients, such as regular networks. The implication is that cryptocurrency networks, which are scale-free, may be uniquely susceptible to double-spending attacks.
Sarada Prasad Gochhayat, Sachin Shetty, Ravi Mukkamala, Peter Foytik · 6 authors
Blockchain promises to provide a distributed and decentralized means of trust among untrusted users. However, in recent years, a shift from decentrality to centrality has been observed in the most accepted Blockchain system, i.e., Bitcoin. This shift has motivated researchers to identify the cause of decentrality, quantify decentrality and analyze the impact of decentrality. In this work, we take a holistic approach to identify and quantify decentrality in Blockchain based systems. First, we identify the emergence of centrality in three layers of Blockchain based systems, namely governance layer, network layer and storage layer. Then, we quantify decentrality in these layers using various metrics. At the governance layer, we measure decentrality in terms of fairness, entropy, Gini coefficient, Kullback-Leibler divergence, etc. Similarly, in the network layer, we measure decentrality by using degree centrality, betweenness centrality and closeness centrality. At the storage layer, we apply a distribution index to define centrality. Subsequently, we evaluate the decentrality in Bitcoin and Ethereum networks and discuss our observations. We noticed that, with time, both Bitcoin and Ethereum networks tend to behave like centralized systems where a few nodes govern the whole network.
Since their introduction, Online Social Networks (OSNs) have transformed the way people interact with each other. Lately, a new trend is rising in the development of OSNs, fueled by an increasing interest of the blockchain technology and the benefits it can bring to the world of OSNs. Blockchain Online Social Media (BOSMs) are Social Media applications that are supported by the blockchain technology. Thanks to a blockchain, BOSMs either try to enforce the privacy of the users or try to redistribute with their users the economic wealth generated by the platform through a rewarding system. There are countless BOSMs available which incorporate a rewarding system. Among them, Steemit can be considered the most well-known platform exceeding 1 million registered users. Steemit is supported by the blockchain Steem, which is a blockchain that natively supports the development of social applications by the usage of transactions that model social activity. Even if other important blockchains, such as Ethereum has been widely analysed, at the best of our knowledge, no study exists concerning the topology of the transactions graph of Steem. The main goal of this paper is to study the structure of the Steem transaction graph to understand its characteristics and unveil crucial knowledge concerning their users. More in detail, we build the Interactions Graph and, after its study, we evaluate three subgraphs that capture its social and monetary aspects. The degree distributions of the graphs follow a power-law. Additionally, we detect a substantial number of bots that offer paid services on the platform among the most active users. Lastly, the investigation of the four analysed graphs through a bow-tie structure, suggesting that half of the users have a passive social behaviour and that 80% of the users tend to accrue economic value
Designed for commercial decentralized applications (DApps), EOSIO is a Delegated Proof-of-Stake (DPoS) based blockchain system. It has overcome some shortages of the traditional blockchain systems like Bitcoin and Ethereum with its outstanding features (e.g., free for usage, high throughput and eco-friendly), and thus becomes one of the mainstream blockchain systems. Though there exist billions of transactions in EOSIO, the ecosystem of EOSIO is still relatively unexplored. To fill this gap, we conduct a systematic graph analysis on the early EOSIO by investigating its four major activities, namely account creation, account vote, money transfer and contract authorization. We obtain some novel observations via graph metric analysis, and our results reveal some abnormal phenomenons like voting gangs and sham transactions.
Marcin Wkatorek, Stanislaw Dro.zd.z, Jarosław Kwapień, Ludovico Minati · 6 authors
The review introduces the history of cryptocurrencies, offering a description of the blockchain technology behind them. Differences between cryptocurrencies and the exchanges on which they are traded have been shown. The central part surveys the analysis of cryptocurrency price changes on various platforms. The statistical properties of the fluctuations in the cryptocurrency market have been compared to the traditional markets. With the help of the latest statistical physics methods the non-linear correlations and multiscale characteristics of the cryptocurrency market are analyzed. In the last part the co-evolution of the correlation structure among the 100 cryptocurrencies having the largest capitalization is retraced. The detailed topology of cryptocurrency network on the Binance platform from bitcoin perspective is also considered. Finally, an interesting observation on the Covid-19 pandemic impact on the cryptocurrency market is presented and discussed: recently we have witnessed a "phase transition" of the cryptocurrencies from being a hedge opportunity for the investors fleeing the traditional markets to become a part of the global market that is substantially coupled to the traditional financial instruments like the currencies, stocks, and commodities. The main contribution is an extensive demonstration that structural self-organization in the cryptocurrency markets has caused the same to attain complexity characteristics that are nearly indistinguishable from the Forex market at the level of individual time-series. However, the cross-correlations between the exchange rates on cryptocurrency platforms differ from it. The cryptocurrency market is less synchronized and the information flows more slowly, which results in more frequent arbitrage opportunities. The methodology used in the review allows the latter to be detected, and lead-lag relationships to be discovered.
For users of the Ethereum network, the gas price is a crucial parameter that determines how swiftly the decentralized consensus protocol confirms a transaction. This paper studies the statistics of the Ethereum gas price. We start with some conceptual discussion of the gas price notion in view of the actual transaction-selection strategies used by Ethereum miners. Subsequently, we provide the descriptive statistics of what we call the threshold gas price. Finally, we identify and estimate a seasonal ARIMA (SARIMA) model for predicting the hourly median of the threshold gas price.
Summary Distinct transactions among different and unrelated users are combined together to create a single Bitcoin transaction (mixing transaction) to obfuscate the relationships among the actual participants (more specifically, the wallet addresses used for the transactions). We consider multi‐input multi‐output transactions with at least two inputs and three outputs as proxy, to analyze four characteristic periods of ∼50 days each, representing periods before the introduction of mixing, in its early days, during its growth, and after the volume of such multi‐input multi‐output transactions became more or less stabile. Structural properties and characteristics of the transaction and wallet address networks are computed and compared, through standard tools, but also via the introduction of two novel techniques that provide indicators of mixing‐like behaviors: (1) an entropy characterization to detect abnormally uniform inputs and/or outputs and (2) a connected component analysis of subgraphs formed by only multi‐input multi‐output transactions (showing cascades of such transactions). The contributions of this exploratory Bitcoin network analysis paper can thus be seen as two‐fold. At a macroscopic level, the growth and stabilization periods are shown to stand out with respect to most considered metrics, while at a microscopic level, chains of multi‐input multi‐output transactions, and transactions with outlier behavior in terms of input/output entropies are identified for further investigation.
Christoph Kinkeldey, Jean‐Daniel Fekete, Tanja Blascheck, Petra Isenberg
We present BitConduite, a visual analytics tool for explorative analysis of financial activity within the Bitcoin network. Bitcoin is the largest cryptocurrency worldwide and a phenomenon that challenges the underpinnings of traditional financial systems - its users can send money pseudo-anonymously while circumventing traditional banking systems. Yet, despite the fact that all financial transactions in Bitcoin are available in an openly accessible online ledger - the blockchain - not much is known about how different types of actors in the network (we call them entities) actually use Bitcoin. BitConduite offers an entity-centered view on transactions, making the data accessible to non-technical experts through a guided workflow for classification of entities according to several activity metrics. Other novelties are the possibility to cluster entities by similarity and exploration of transaction data at different scales, from large groups of entities down to a single entity and the associated transactions. Two use cases illustrate the workflow of the system and its analytic power. We report on feedback regarding the approach and the the software tool gathered during a workshop with domain experts, and we discuss the potential of the approach based on our findings.
In this work we analyze the blockchain forking events, blockchain partitioning, and duration of inconsistent state of the ledger in a Bitcoin delivery network. Using a comprehensive probabilistic model, we obtain the probability distribution of two- and three-way forks, the forked partition sizes, and the duration of ledger inconsistency until the resolution. We show that the three-way forking probability is substantially lower than that of a two-way forking and that the partition sizes in the case of two-way forking tend to equalize when the number of nodes increases. Finally, we show that the duration of ledger inconsistency state exhibits long tail probability distribution which means that successive forking events can force the ledger to remain inconsistent for long time.
Blockchain technology and, in particular, blockchain-based cryptocurrencies offer us information that has never been seen before in the financial world. In contrast to fiat currencies, all transactions of crypto-currencies and crypto-tokens are permanently recorded on distributed ledgers and are publicly available. As a result, this allows us to construct a transaction graph and to assess not only its organization but to glean relationships between transaction graph properties and crypto price dynamics. The ultimate goal of this paper is to facilitate our understanding on horizons and limitations of what can be learned on crypto-tokens from local topology and geometry of the Ethereum transaction network whose even global network properties remain scarcely explored. By introducing novel tools based on topological data analysis and functional data depth into Blockchain Data Analytics, we show that Ethereum network (one of the most popular blockchains for creating new crypto-tokens) can provide critical insights on price strikes of crypto-tokens that are otherwise largely inaccessible with conventional data sources and traditional analytic methods.
United Nations security council resolutions are voted on by fifteen members of the security council. A resolution will be deemed to be adopted if more than nine of the member states adopt it and all five permanent members do not use the veto. The five permanent members can vote yes, no, and abstain against a resolution. The vague set can be explained intuitively with the voting model, indicating the three conditions for voting. Which are favor, against, and abstention. When the agent nodes were selected by the consensus mechanism of delegated proof of stake (DPoS), the voting node can only vote support instead of against and abstention, and there are no penalties for malicious nodes. Besides, there is no clear method to select the agent node if the votes obtained by the last agent node are the same as the votes cast by others. This article puts forward a kind of vague set to improve DPoS consensus mechanism. Just like how human elections are held, this method allows each node to vote for the agent node. This is a fair and effective way of selecting agent node when alternate nodes are of the same value, it improves the security and fairness of blockchain, reduces the probability that malicious nodes will be selected as agent nodes. We prove that the maximum probability of nodes after the vote of the fuzzy membership degree is 0.5. The feasibility and effectiveness of the improved consensus mechanism is verified by examples.
The consensus is a more important part of the block chain. DPoS(Delegated Proof of Stake), one of the mainstream consensus, solves the problem of the waste of power and centralization of PoW(Proof of Work) and PoS(Proof of Stake). However, there is still a problem that the node voting is not active and the voting reference is not sufficient. In response to these problems, this paper proposes a new reward method and an additional reference basis. It aims to enhance the enthusiasm of the node voting by increasing the reward for the node. Combining the attribute values of the nodes in the side chain enables the nodes to have a richer reference basis that can be selected by self-judgment when voting. Experiments show that the extra reward mechanism can bring more rewards to actively participate in voting nodes and increase the enthusiasm of node voting.
The aim of this work is to analyze the major existing cryptocurrency consensus algorithms considering a number ofattributes that may play a significant role in the long-term sustainability of a cryptocurrency ecosystem and to comparativelyevaluate
Abstract Hacks are one of the most damaging types of cryptocurrency related crime, accounting for billions of dollars in stolen funds since 2009. Professional investigators at Chainalysis have traced these stolen funds from the initial breach on an exchange to off-ramps, i.e. services where criminals are able to convert the stolen funds into fiat or other cryptocurrencies. We analyzed six hack subnetworks of bitcoin transactions known to belong to two prominent hacking groups. We analyze each hack according to eight network features, both static and temporal, and successfully classify each hack to its respective hacking group through our newly proposed method. We find that the static features, such as node balance, in degree, and out degree are not as useful in classifying the hacks into hacking groups as temporal features related to how quickly the criminals cash out. We validate our operating hypothesis that the key distinction between the two hacking groups is the acceleration with which the funds exit through terminal nodes in the subnetworks.
Abstract Cryptocurrencies as a new way of transferring assets and securing financial transactions have gained popularity in recent years. Transactions in cryptocurrencies are publicly available, hence, statistical studies on different aspects of these currencies are possible. However, previous statistical analysis on cryptocurrencies transactions have been very limited and mostly devoted to Bitcoin, with no comprehensive comparison between these currencies. In this study, we intend to compare the transaction graph of Bitcoin, Ethereum, Litecoin, Dash, and Z-Cash, with respect to the dynamics of their transaction graphs over time, and discuss their properties. In particular, we observed that the growth rate of the nodes and edges of the transaction graphs, and the density of these graphs, are closely related to the price of these currencies. We also found that the transaction graph of these currencies is non-assortative, i.e. addresses do not tend for transact with a particular type of addresses of higher or lower degree, and the degree sequence of their transaction graph follows the power law distribution.
In recent years, increasing attention has been devoted to cryptocurrencies, owing to their great development and valorization. In this study, we propose to analyse four of the major cryptocurrencies, based on their market capitalization and data availability: Bitcoin, Ethereum, Ripple, and Litecoin. We apply detrended fluctuation analysis (the regular one and with a sliding windows approach) and detrended cross-correlation analysis and the respective correlation coefficient. We find that Bitcoin and Ripple seem to behave as efficient financial assets, while Ethereum and Litecoin present some evidence of persistence. When correlating Bitcoin with the other cryptocurrencies under analysis, we find that for short time scales, all the cryptocurrencies have statistically significant correlations with Bitcoin, although Ripple has the highest correlations. For higher time scales, Ripple is the only cryptocurrency with significant correlation.