We address two problems: first, we study a variant of block withholding (BWH) attack in Bitcoins and second, we propose solutions to prevent all existing types of BWH attacks in Bitcoins. We analyze ...the strategies of a selfish Bitcoin miner who in connivance with one pool attacks another pool and receives reward from the former mining pool for attacking the latter. We name this attack as "sponsored block withholding attack." We present detailed quantitative analysis of the monetary incentive that a selfish miner can earn by adopting this strategy under different scenarios. We prove that under certain conditions, the attacker can maximize her revenue by adopting some strategies and by utilizing her computing power wisely. We also show that an attacker may use this strategy for attacking both the pools for earning higher amount of incentives. More importantly, we present a strategy that can effectively counter block withholding attack in any mining pool. First, we propose a generic scheme that uses cryptographic commitment schemes to counter BWH attack. Then, we suggest an alternative implementation of the same scheme using hash function. Our scheme protects a pool from rogue miners as well as rogue pool administrators. The scheme and its variant defend against BWH attack by making it impossible for the miners to distinguish between a partial proof of work and a complete proof of work. The scheme is so designed that the administrator cannot cheat on the entire pool. The scheme can be implemented by making minor changes to existing Bitcoin protocol. We also analyze the security of the scheme.
An increased capacity to produce renewable energy has led to power curtailments due to the lack of storage for energy oversupply. This excess energy could make a profit if it was used and managed ...effectively. Bitcoin, with its recent boom, associated market values, and excessive energy consumption during mining presents a win–win proposition for managing renewable energy curtailments. Therefore, in a bid to minimize renewable energy curtailments from the perspective of the independent system operator (ISO) while maximizing the profit for the investor, this study attempts to discover the optimal planning and operation of a bitcoin mining farm to minimize renewable energy curtailments. Specifically, renewable energy curtailments for the Energy Reliability Council of Texas (ERCOT) at an hourly resolution and the difficulty and price, respectively of bitcoin mining during 2020 and 2021, were considered in our analysis of the cost and profitability of bitcoin mining using curtailed renewable power. Besides, a greenhouse gas (GHG) analysis was also conducted to evaluate the annual emissions from each of the cases considered in this study. The results demonstrated that 93% of the curtailed energy could be used at a minimal cost to generate a $239 million profit. From an investor's perspective, 69.8% of the curtailed power could be used to generate a profit of $605 million. Cost minimization case scenario had the least amount of emissions followed by profit maximization case with penalty scenario. Sensitivity analyses and Monte-Carlo simulations were performed to investigate the effect of system parameters on the optimization results for an in-depth analysis of possible policy and investment decisions from the perspectives of both the ISOs and investors. Despite the uncertainties associated with the price of bitcoin, it was estimated that ERCOT, with its current renewable energy curtailments, would still be profitable in the case of profit maximization if the bitcoin price remains above $6800 throughout the year. Accordingly, bitcoin mining has substantial potential for becoming an effective medium to prevent renewable energy curtailments and turn energy oversupply into profit.
Blockchain and cryptocurrency are a hot topic in today’s digital world. In this paper, we create a game theoretic model in continuous time. We consider a dynamic game model of the bitcoin market, ...where miners or players use mining systems to mine bitcoin by investing electricity into the mining system. Although this work is motivated by BTC, the work presented can be applicable to other mining systems similar to BTC. We propose three concepts of dynamic game theoretic solutions to the model:
Social optimum
,
Nash equilibrium
and
myopic Nash equilibrium
. Using the model that a player represents a single “miner” or a “mining pool”, we develop novel and interesting results for the cryptocurrency world.
This study estimates the environmental impacts of Bitcoin mining. Employing a top-down measurement approach, this paper assesses the carbon footprint of Bitcoin mining in China from 2017 to 2021. The ...findings reveal that mining activities during this period contributed to a total of 77.84 million tons of carbon dioxide emissions in China. By utilizing data at the provincial level, we find that the seasonal migration of Bitcoin mining pools will lead to regional power demand shocks in China. Additionally, this study predicts future carbon emissions from Bitcoin mining in China, projecting cumulative carbon dioxide emissions of 76.40 million tons and 722.18 million tons by 2030 and 2060 respectively, in the absence of any policy interventions. Based on these findings, this paper posits that governments worldwide should make efforts to restrict the carbon emissions from Bitcoin mining and opt for environmentally friendly technological methods to fundamentally alleviate Bitcoin's reliance on energy. The implication for central banks is that carbon emission should be taken into consideration when designing the central bank digital currencies (CBDCs).
2022 has seen a significant decline in global cryptocurrency ratings, especially bitcoin. As it is known, one of the key components of the cryptocurrency’s cost is the amount of electrical energy ...spent by the computing equipment of the mining data centers. In the context of declining bitcoin rates, the management of the data centers’ energy costs becomes critical for maintaining the profitability and investment return of the mining projects. Russia is among the top-3 leading producers of cryptocurrencies, providing 11% of the global primary bitcoin transactions. In this regard, the management of the data centers’ costs related to the purchase of electricity in the Russian wholesale and retail electricity (capacity) markets presents a high scientific and practical importance. This article analyzes the pricing mechanisms for the purchase of electricity in Russia’s wholesale and retail electricity (capacity) markets on an industrial scale given the specifics of the hourly demand-based pricing. This paper suggests a new metrics system, including a capacity demand management coefficient and a transmission cost management coefficient, which allow setting specific price parameters for the different components of the electricity price based on demand analytics. Simulation of different parameters of the energy cost management in mining data centers demonstrated that the ultimate electricity price can, on average, be reduced by 70% of the initial level across all regions of the Siberian Federal District of Russia. The suggested energy cost management model takes into account both internal and external factors of industrial data centers as well as monitoring of their operations along with the price factors of the wholesale and retail electricity markets. This material may be useful to specialists in the field of management of mining data centers who are involved in the operation and/or design of such facilities across various regions of Russia.
This study estimates the environmental impact of mining Bitcoin, the most well-known blockchain-based cryptocurrency, and contributes to the discussion on the technology’s supposedly large energy ...consumption and carbon footprint. The lack of a robust methodological framework and of accurate data on key factors determining Bitcoin’s impact have so far been the main obstacles in such an assessment. This study applied the well-established Life Cycle Assessment methodology to an in-depth analysis of drivers of past and future environmental impacts of the Bitcoin mining network. It was found that, in 2018, the Bitcoin network consumed 31.29 TWh with a carbon footprint of 17.29 MtCO2-eq, an estimate that is in the lower end of the range of results from previous studies. The main drivers of such impact were found to be the geographical distribution of miners and the efficiency of the mining equipment. In contrast to previous studies, it was found that the service life, production, and end-of-life of such equipment had only a minor contribution to the total impact, and that while the overall hashrate is expected to increase, the energy consumption and environmental footprint per TH mined is expected to decrease.
Cryptocurrencies represented by Bitcoin have fully demonstrated their advantages and great potential in payment and monetary systems during the last decade. The mining pool, which is considered the ...source of Bitcoin, is the cornerstone of market stability. The surveillance of the mining pool can help regulators effectively assess the overall health of Bitcoin and issues. However, the anonymity of mining-pool miners and the difficulty of analyzing large numbers of transactions limit in-depth analysis. It is also a challenge to achieve intuitive and comprehensive monitoring of multi-source heterogeneous data. In this study, we present SuPoolVisor, an interactive visual analytics system that supports surveillance of the mining pool and de-anonymization by visual reasoning. SuPoolVisor is divided into pool level and address level. At the pool level, we use a sorted stream graph to illustrate the evolution of computing power of pools over time, and glyphs are designed in two other views to demonstrate the influence scope of the mining pool and the migration of pool members. At the address level, we use a force-directed graph and a massive sequence view to present the dynamic address network in the mining pool. Particularly, these two views, together with the Radviz view, support an iterative visual reasoning process for de-anonymization of pool members and provide interactions for cross-view analysis and identity marking. Effectiveness and usability of SuPoolVisor are demonstrated using three cases, in which we cooperate closely with experts in this field.
Mining pools have become dominant in today's bitcoin mining network, where miners can pool their powers together for reduced variance of block mining and steadier stream of potential income. Along ...with the continuous evolvement of mining pools are the increasingly intense competitions among them. Recent empirical studies have shown that the distributed denial-of-service (DDoS) attack is one of the most common ways for competing mining pools to sabotage the rivals and earn illegitimate rewards. Existing efforts have been made on using static game models to analyze the interactions between mining pools, and derive the Nash Equilibrium and optimal attacking strategies in a one-time static context. To better understand the impact of such DDoS attacks, in this paper, we take a starkly different approach, and for the first time address the dynamics in mining pool attacks. Specifically, we start by formulating the interactive competition among mining pools as a general-sum stochastic game. Then we propose an efficient Nash learning algorithm to obtain the near optimal attacking strategy that maximizes the expected long-term utility. Our theoretical analysis and extensive experimental results both show that the proposed strategy outperforms the baseline myopic learning algorithm, which only aims at maximizing the revenue in the current time stage. These findings, together with our proposed stochastic game model and learning algorithm, are expected to provide more practical guidelines for mining pools to survive and thrive in the highly-competitive bitcoin ecosystem.
Blockchain technology revolutionized the financial system with the emergence of cryptocurrencies. Bitcoin as the most used cryptocurrency has been particularly questioned in the literature for its ...sustainability. Very few studies explore the future of sustainability from the perspective of both miners' and technology makers' continual profitability. Our study evaluates the hardware used for Bitcoin mining from a sustainability analysis perspective; therefore, changes in the difficulty level in the block algorithm and mining hardware's thermal power usage are considered to determine the profitability and the exergetic efficiency of the mining process. The consistent decay in exergetic efficiency curves of several GPU and ASIC-based technology hardware suggest that the hardware can be utilized up to the levels where the efficiency values hit zero. Simultaneously, the diminishing profit levels over the course of changing Bitcoin prices make the mining hardware unsustainable signaling an end to its life cycle and switching the hardware with more efficient ones. Our findings shed light on the future of the sustainability of Bitcoin mining in terms of securing the profitability of the business by determining the exergetic efficiency of the hardware with a completely new perspective in the literature.
•The sustainability of Bitcoin mining is evaluated from a thermodynamics perspective.•Difficulty change in block mining affects the magnitude of exergy destruction.•Exergetic efficiency analysis indicates the profitable lifetime of mining hardware.•The exergetic efficiency of the mining hardware identifies the time to switch mining hardware.
•We perform predictive modeling of environmental footprint in bitcoin mining.•We propose AI driven modeling frameworks.•We identify the key variables that drive environmental footprint.•Block size of ...the blockchain is the most important predictor.•The results will help to monitor, control, and reduce the environmental footprint.
The Bitcoin mining hosted in the blockchain network consumes enormous amounts of energy and generates electronic waste at an alarming rate. The paper aims to model and predict the future values of these two hazardous variables linked to conventional Bitcoin mining. We develop two predictive models using Facebook's Prophet algorithm and deep neural networks to identify and explain energy consumption and electronic waste generation patterns. The models rely on several explanatory features linked to the blockchain microstructure and the Bitcoin marketplace. We assess the predictive performance of the two models based on daily data of energy consumption and electronic waste generation and eleven key input features. We use local interpretable model-agnostic explanation (LIME) and Shapley additive explanation (SHAP) for explaining how these inputs can predict and control energy consumption and electronic waste generation. The findings assist in accurately estimating the future figures of energy discharge and electronic waste accumulation in the present Bitcoin mining setup. The study also reveals the block size to be the major driver.
PDF
