Mining Rewards Research Articles

Cryptocurrency blockchain and its carbon footprint: Anticipating future challenges

This study investigates the environmental impact of cryptocurrency blockchain technology, specifically focusing on the adoption of Bitcoin and Ethereum. Utilizing instrumental regression analysis, it examines the consensus mechanisms—Proof of Work (PoW) and Proof of Stake (PoS)—and their association with carbon footprint. The findings reveal a clear correlation between the adoption of PoW-based cryptocurrencies like Bitcoin and carbon emissions. Notably, PoW-based cryptocurrencies generate approximately 0.86 metric tons of carbon emissions per transaction, which is significantly higher than PoS-based transactions like Ethereum. Concerns also arise regarding the sustainability of Bitcoin mining after the fourth halving period and potential shortages in PoW-based mining resources. As mining rewards decrease, the challenge of timely block creation intensifies. By projecting future BTC mining phases, the study anticipates diminishing rewards in each halving period, potentially leaving around 2500 BTC units available for mining by 2120. This suggests that achieving the full 21 million BTC may prove practically unattainable within the current blockchain framework. Furthermore, the research highlights the speculative and risky nature of cryptocurrencies lacking regulatory oversight, which tends to attract irresponsible investors. It stresses the importance of developing a nuanced understanding of the environmental implications of PoW cryptocurrencies and advocating for responsible innovation. Additionally, the study underscores the necessity of considering the monetary policy implications and raising awareness among investors to ensure responsible decision-making.

Is Bitcoin Future as Secure as We Think? Analysis of Bitcoin Vulnerability to Bribery Attacks Launched through Large Transactions

Bitcoin uses blockchain technology to maintain transactions order and provides probabilistic guarantees to prevent double-spending, assuming that an attacker’s computational power does not exceed 50% of the network power. In this article, we design a novel bribery attack and show that this guarantee can be hugely undermined. Miners are assumed to be rational in this setup, and they are given incentives that are dynamically calculated. In this attack, the adversary misuses the Bitcoin protocol to bribe miners and maximize their gained advantage. We will reformulate the bribery attack to propose a general mathematical foundation upon which we build multiple strategies. We show that, unlike Whale Attack, these strategies are practical, especially in the future when halvings lower the mining rewards. In the so-called “guaranteed variable-rate bribing with commitment” strategy, through optimization by Differential Evolution (DE), we show how double-spending is possible in the Bitcoin ecosystem for any transaction whose value is above 218.9BTC, and this comes with 100% success rate. A slight reduction in the success probability, e.g., by 10%, brings the threshold down to 165BTC. If the rationality assumption holds, then this shows how vulnerable blockchain-based systems like Bitcoin are. We suggest a soft fork on Bitcoin to fix this issue at the end.

Novel MITM attack scheme based on built-in negotiation for blockchain-based digital twins

As smart contracts, represented by Solidity, become deeply integrated into the manufacturing industry, blockchain-based Digital Twins (DT) has gained momentum in recent years. Most of the blockchain infrastructures in widespread use today are based on the Proof-of-Work (PoW) mechanism, and the process of creating blocks is known as “mining”. Mining becomes increasingly difficult as the blockchain grows in size and the number of on-chain business systems increases. To lower the threshold of participation in the mining process, “mining pools” have been created. Miners can cooperate and share the mining rewards according to the hashrate they contributed to the pool. Stratum is the most widely used communication protocol between miners and mining pools. Its security is essential for the participants. In this paper, we propose two novel Man-In-The-Middle (MITM) attack schemes against Stratum, which allow attackers to steal miners' hashrate to any mining pool using hijacked TCP connections. Compared with existing attacks, our work is more secretive, more suitable for the real-world environment, and more harmful. The Proof-of-Concept (PoC) shows that our schemes work perfectly on most mining softwares and pools. Furthermore, we present a lightweight AI-driven approach based on protocol-level feature analysis to detect Stratum MITM for blockchain-based DTs. Its detection model consists of three layers: feature extraction layer, vectorization layer, and detection layer. Experiments prove that our detection approach can effectively detect Stratum MITM traffic with 98% accuracy. Our work alerts the communities and provides possible mitigation against these more hidden and profitable attack schemes.

Less Is More: Understanding Network Bias in Proof-of-Work Blockchains

Blockchains are becoming increasingly important in today’s Internet, enabling large-scale decentralized applications with strong security and transparency properties. In a blockchain system, participants maintain and update the server-side state of an application by appending data as blocks onto an immutable, distributed ledger through a consensus protocol within a peer-to-peer network. There has been a significant increase in profit in mining blocks. For instance, Bitcoin miners currently receive over USD 200,000 per mined block. An essential determinant of these rewards is the time it takes to disseminate newly mined blocks across the network. This paper addresses the challenge of optimizing mining rewards by exploring topology design in a wide-area blockchain network utilizing a Proof-of-Work consensus protocol. We show that under low block times, the geographical location of a miner critically impacts the number of successful blocks mined by the miner. We also show that a miner may improve its success rate by increasing its connectivity to the network. However, contrary to the general wisdom that a faster network is always better for a miner, we show that increasing network connectivity (e.g., by adding more neighbors) is beneficial to a miner only up to a point after which the miner’s rewards degrade. This is because when a miner improves its connectivity, it inadvertently also aids other miners in increasing their connectivity. We also present a network-level collusion attack in which a miner can increase its block success rate by becoming part of a tightly connected cluster. Here too, we observe that the mining gains obtained increase with cluster size only up to a point, and decrease thereafter. Our findings highlight that the network topology is a key variable affecting miner performance in PoW blockchains that must not be overlooked. We demonstrate our observations via detailed simulations modeled using real-world measurement data.

Bitcoin transaction fees and the decentralization of Bitcoin mining pools

In this paper, we propose a novel mechanism for decentralizing Bitcoin mining pools: the uncertain mining reward resulting from Bitcoin transaction fees. We present a simple model to demonstrate that risk-averse Bitcoin miners are more likely to allocate their computational power across multiple mining pools when transaction fees make up a large fraction of the mining reward. Our empirical study shows a negative relatinship between the proportion of transaction fees and the decentralization of Bitcoin mining pools. Our study suggests that an active Bitcoin trading market or a decreasing fixed reward from mining would lead Bitcoin mining pools to be more decentralized.

Proof-of-Work Cryptocurrencies: Does Mining Technology Undermine Decentralization?

Does the proof-of-work consensus protocol serve its intended purpose of supporting decentralized cryptocurrency mining? To address this question, we develop a game-theoretical model in which miners first invest in hardware to improve the efficiency of their operations and then compete for mining rewards in a rent-seeking game. We show that centralization grows with heterogeneity in mining costs, but hardware capacity constraints prevent the most efficient miners from monopolizing the mining process. Investment leads to a more decentralized network unless larger miners have a significant comparative advantage in acquiring new hardware. Our model generates empirically supported implications: (i) mining centralization is countercyclical with respect to mining reward, and (ii) a change in mining reward leads to a less-than-proportional change in hash rates. This paper was accepted by David Simchi-Levi, Special Section of Management Science: Blockchains and Crypto Economics. Supplemental Material: The data file is available at https://doi.org/10.1287/mnsc.2023.4840 .

An Evolutionary Game Theory-Based Method to Mitigate Block Withholding Attack in Blockchain System

Consensus algorithms are the essential components of blockchain systems. They guarantee the blockchain’s fault tolerance and security. The Proof of Work (PoW) consensus algorithm is one of the most widely used consensus algorithms in blockchain systems, using computational puzzles to enable mining pools to compete for block rewards. However, this excessive competition for computational power will bring security threats to blockchain systems. A block withholding (BWH) attack is one of the most critical security threats blockchain systems face. A BWH attack obtains the reward of illegal block extraction by replacing full proof with partial mining proof. However, the current research on the BWH game could be more extensive, considering the problem from the perspective of a static game, and it needs an optimal strategy that dynamically reflects the mining pool for multiple games. Therefore, to solve the above problems, this paper uses the method of the evolutionary game to design a time-varying dynamic game model through the degree of system supervision and punishment. Based on establishing the game model, we use the method of replicating dynamic equations to analyze and find the optimal strategy for mining pool profits under different BWH attacks. The experimental results demonstrate that the mining pools will choose honest mining for the best profit over time under severe punishment and high supervision. On the contrary, if the blockchain system is supervised with a low penalty, the mining pools will eventually choose to launch BWH attacks against each other to obtain the optimal mining reward. These experimental results also prove the validity and correctness of our model and solution.

A Mean Field Games Model for Cryptocurrency Mining

We propose a mean field game model to study the question of how centralization of reward and computational power occur in Bitcoin-like cryptocurrencies. Miners compete against each other for mining rewards by increasing their computational power. This leads to a novel mean field game of jump intensity control, which we solve explicitly for miners maximizing exponential utility and handle numerically in the case of miners with power utilities. We show that the heterogeneity of their initial wealth distribution leads to greater imbalance of the reward distribution, and increased wealth heterogeneity over time, or a “rich get richer” effect. This concentration phenomenon is aggravated by a higher Bitcoin mining reward and reduced by competition. Additionally, an advantaged miner with cost advantages such as access to cheaper electricity, contributes a significant amount of computational power in equilibrium, unaffected by competition from less efficient miners. Hence, cost efficiency can also result in the type of centralization seen among miners of cryptocurrencies. This paper was accepted by Kay Giesecke, finance. Funding: A. M. Reppen is partly supported by the Swiss National Science Foundation [Grant SNF 181815]. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2023.4798 .

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.

Effective Selfish Mining Defense Strategies to Improve Bitcoin Dependability

Selfish mining is a typical malicious attack targeting the blockchain-based bitcoin system, an emerging crypto asset. Because of the non-incentive compatibility of the bitcoin mining protocol, the attackers are able to collect unfair mining rewards by intentionally withholding blocks. The existing works on selfish mining mostly focused on cryptography design, and malicious behavior detection based on different approaches, such as machine learning or timestamp. Most defense strategies show their effectiveness in the perspective of reward reduced. No work has been performed to design a defense strategy that aims to improve bitcoin dependability and provide a framework for quantitively evaluating the improvement. In this paper, we contribute by proposing two network-wide defensive strategies: the dynamic difficulty adjustment algorithm (DDAA) and the acceptance limitation policy (ALP). The DDAA increases the mining difficulty dynamically once a selfish mining behavior is detected, while the ALP incorporates a limitation to the acceptance rate when multiple blocks are broadcast at the same time. Both strategies are designed to disincentivize dishonest selfish miners and increase the system’s resilience to the selfish mining attack. A continuous-time Markov chain model is used to quantify the improvement in bitcoin dependability made by the proposed defense strategies. Statistical analysis is applied to evaluate the feasibility of the proposed strategies. The proposed DDAA and ALP methods are also compared to an existing timestamp-based defense strategy, revealing that the DDAA is the most effective in improving bitcoin’s dependability.

The Inevitability of Escalating Energy Usage for Popular Proof-of-Work Cryptocurrencies

Few financial market innovations offer the opportunities to enhance transaction efficiency, as does the potential of the blockchain and cryptocurrencies. The author demonstrates that this industry's unusual demand and supply relationship results in ever-increasing energy consumption if Proof-of-Work currencies rise in price faster than mining rewards decay. This previously undocumented challenge poses global warming risk. In addition, the accessibility and opacity of many cryptocurrencies create financial risks for inexperienced speculators. Digital currencies also reduce the ability of central banks to conduct monetary policy and challenge regulatory authorities to promulgate rules to protect the economy and consumers. The author explores these various risks, demonstrates how the design of Proof-of-Work cryptocurrency mining frustrates the efficiency they promise and describes mechanisms that would allow cryptocurrencies based on the blockchain to live up to their immense potential.

Stability Analysis and Control of Decision-Making of Miners in Blockchain

To maintain blockchain-based services with ensuring its security, it is an important issue how to decide a mining reward so that the number of miners participating in the mining increases. We propose a dynamical model of decision-making for miners using an evolutionary game approach and analyze the stability of equilibrium points of the proposed model. The proposed model is described by the 1st-order differential equation. So, it is simple but its theoretical analysis gives an insight into the characteristics of the decision-making. Through the analysis of the equilibrium points, we show the transcritical bifurcations and hysteresis phenomena of the equilibrium points. We also design a controller that determines the mining reward based on the number of participating miners to stabilize the state that all miners participate in the mining. Numerical simulation shows that there is a trade-off in the choice of the design parameters.

Towards Detection of Selfish Mining Using Machine Learning

Selfish mining is an attack against a blockchain where miners hide newly discovered blocks instead of publishing them to the rest of the network. The selfish miners continue to mine on their private chain while the honest miners waste resources mining on a shorter chain. According to the blockchain protocol, a longer chain takes precedent and shorter chains are discarded which allows the selfish miners to gain an advantage by keeping their chain secret. This attack can be used by malicious miners to earn a disproportionate share of the mining rewards or in conjunction with other attacks to steal money from cryptocurrency exchanges. Several of these attacks were launched in 2018 and 2019 with the attackers stealing as much as $18 Million. Developers made several different attempts to fix this issue, but the effectiveness of the fixes is currently unknown. Although this attack is possible against both Proof-of-Work and Proof-of-Stake blockchains, this research concentrates on detection in Proof-of-Work blockchains. As is difficult to evaluate security advances in the real-time blockchain, it is imperative to focus on simulation to evaluate blockchain security properties. To this end, we extend a blockchain simulator and add the ability to simulate selfish mining attacks. Several existing simulators are examined before choosing SimBlock for this research. Our goal is to identify the factors that identify selfish mining. Using existing research, we choose several factors that could identify an attack in an unlaunched state, an active state, or historically. We plan to use simulated data to train a machine learning model to detect selfish mining. Using the modified simulator, we generate training and test data for unlaunched and active attacks. For historical attacks, we will use historical data from known selfish mining attacks. While some existing research has examined the detection of selfish mining, it only examines active attacks. In this paper, we seek to lay the groundwork for future research into detecting attacks that are unlaunched, active, or historical.

Bitcoin: A Natural Oligopoly

We argue that the concentrated production and ownership of Bitcoin mining hardware arise naturally from the economic incentives of Bitcoin mining. We model Bitcoin mining as a two-stage competition; miners compete in prices to sell hardware while competing in quantities for mining rewards. We characterize equilibria in our model and show that small asymmetries in operational costs result in highly concentrated ownership of mining equipment. We further show that production of mining equipment will be dominated by the miner with the most efficient hardware, who will sell hardware to competitors while possibly also using it to mine. This paper was accepted by Kay Giesecke, finance.

On the Use of Proof-of-Work in Permissioned Blockchains: Security and Fairness

In permissioned blockchains, a set of identifiable miners validates transactions and creates new blocks. In scholarship, the proposed solution for the consensus protocol is usually inspired by the Byzantine fault tolerance (BFT) based on voting rather than the proof-of-work (PoW). The advantage of PoW with respect to BFT is that it allows the final user to evaluate the cost required to change a confirmed transaction without the need to trust the consortium of miners. In this paper, we analyse the problems that arise from the application of PoW in permissioned blockchains. In standard PoW, it may be easy for colluded miners to temporarily reach 50% of the total hash power (HP). Moreover, since mining rewards are not usually expected in permissioned contexts, the problem of balancing the computational efforts among the miners becomes crucial. We propose a solution based on a sliding window algorithm to address these problems and analyse its effectiveness in terms of fairness and security. Furthermore, we present a quantitative, analytical model in order to assess its capacity to balance the hash power provided by heterogeneous miners. Our study considers the trade-off between the need to trust the entire consortium of miners guaranteed by the global HP invested by the mining process and the need to prevent collusion among malicious miners aimed at reaching 50% of the total HP. As a result, the model can be used to find the optimal parameters for the sliding window protocol.

Distributed Hybrid Double-Spending Attack Prevention Mechanism for Proof-of-Work and Proof-of-Stake Blockchain Consensuses

Blockchain technology is a sustainable technology that offers a high level of security for many industrial applications. Blockchain has numerous benefits, such as decentralisation, immutability and tamper-proofing. Blockchain is composed of two processes, namely, mining (the process of adding a new block or transaction to the global public ledger created by the previous block) and validation (the process of validating the new block added). Several consensus protocols have been introduced to validate blockchain transactions, Proof-of-Work (PoW) and Proof-of-Stake (PoS), which are crucial to cryptocurrencies, such as Bitcoin. However, these consensus protocols are vulnerable to double-spending attacks. Amongst these attacks, the 51% attack is the most prominent because it involves forking a blockchain to conduct double spending. Many attempts have been made to solve this issue, and examples include delayed proof-of-work (PoW) and several Byzantine fault tolerance mechanisms. These attempts, however, suffer from delay issues and unsorted block sequences. This study proposes a hybrid algorithm that combines PoS and PoW mechanisms to provide a fair mining reward to the miner/validator by conducting forking to combine PoW and PoS consensuses. As demonstrated by the experimental results, the proposed algorithm can reduce the possibility of intruders performing double mining because it requires achieving 100% dominance in the network, which is impossible.

Adjusting the Block Interval in PoW Consensus by Block Interval Process Improvement

Blockchain is not widely applied in various fields due to the critical issue of scalability as part of the blockchain trilemma. This issue arises during consensus among the nodes in a public blockchain. To address the issue of low scalability with proof-of-work (PoW) consensus, various methods have been proposed for transaction per second (TPS) improvement. However, no such methods include an improvement in the consensus step. Therefore, to improve PoW public blockchain scalability, it is important to shorten the time required for PoW consensus. This paper proposes a method for minimizing the block intervals that occur during consensus over a PoW blockchain network. A shortened block interval leads to an increase in the probability of three different attacks: selfish mining, double-spending, and eclipse attacks. According to an experiment using Ethereum, with a typical PoW blockchain, it is inevitable to provide rewards for stable block mining in competition between mining pools. To find an optimal block interval in the PoW consensus algorithm, we conducted a four-step experiment. The purpose of this experiment was to verify the difficulty level and issues with Mainnet security. Therefore, considering stale block mining rewards, an optimal block interval is proposed. The Ethereum TPS was improved by at least 200%. Given this finding, it is considered possible to achieve a similar improvement in a different PoW blockchain. On balance, even if the block interval is shorter than that of the PoW Mainnet, network security falls by only 1.21% in Testnet, even with a rise in the stale block rate, while performance is increased at up to 120 TPS, which is three times higher than that in Mainnet.

PPChain: A Privacy-Preserving Permissioned Blockchain Architecture for Cryptocurrency and Other Regulated Applications

Existing (popular) blockchain architectures, including the widely used Ethereum and Hyperledger, are generally not designed to achieve conflicting properties such as anonymity and regulation, and transparency and confidentiality. In this article, we propose a privacy-preserving permissioned blockchain architecture (PPChain) that permits one to also introduce regulation, where PPChain's architecture is modified from that of Ethereum. Specifically, we integrate the cryptographic primitives (group signature and broadcast encryption), and adopt practical byzantine fault tolerance consensus protocol with a validate-record separation mechanism, as well as removing the transaction fee and mining reward. To show the utility of PPChain, we provide qualitative security and privacy analysis, and performance analysis. We also explain how PPChain can be deployed in regulation applications, using cryptocurrency, food supply chain, and sealed-bid auctions as examples.

The economic dependency of bitcoin security

ABSTRACT We studied the extent to which bitcoin blockchain security permanently depends on the underlying distribution of cryptocurrency market outcomes using daily blockchain and bitcoin data for 2014–2019 and employing the autoregressive-distributed lag (ARDL) approach. We tested three equilibrium hypotheses: (i) sensitivity of the bitcoin blockchain to mining reward, (ii) security outcomes of the bitcoin blockchain and the proof-of-work cost, and (iii) the speed of adjustment of the bitcoin blockchain security to deviations from the equilibrium path. Our results suggest that bitcoin price and mining rewards were intrinsically linked to bitcoin security outcomes.The bitcoin blockchain security’s dependency on mining costs was geographically differenced – it was more significant for the global mining leader China than for other world regions. Bitcoin blockchain security tended to revert relatively fast to its equilibrium security level after the input or output of price shocks.

An Equilibrium Model of the Market for Bitcoin Mining

We propose a model that uses the exchange rate of Bitcoin against the US dollar to predict the computing power of Bitcoin’s network. We show that free entry places an upper bound on mining revenues and explain how it can be identified. Calibrating the model’s parameters allows us to accurately forecast the evolution of the network computing power over time. We find that a significant share of mining rewards was invested in mining equipment and that the seigniorage income of miners was limited.