The mining process in the Blockchain is very resource intensive, therefore, miners form coalitions to verify each block of transactions in return for a reward where only the first coalition that accomplishes the proof-of-work will be rewarded. This leads to intense competitions among miners and consequently dishonest mining strategies, such as block withholding attack, selfish mining, eclipse attack and stubborn mining, to name a few. As a result, it is necessary to regulate the mining process to make miners accountable for any dishonest mining behavior. We therefore propose a new reputation-based framework for the proof-of-work computation in the Blockchain in which miners not only are incentivized to conduct honest mining but also disincentivized to commit to any malicious activities against other mining pools. We first illustrate the architecture of our reputation-based paradigm, explain how the miners are rewarded or penalized in our model, and subsequently, we provide game theoretical analyses to show how this new framework encourages the miners to avoid dishonest mining strategies. In our setting, a mining game is repeatedly played among a set of pool managers and miners where the reputation of each miner or mining ally is continuously measured. At each round of the game, the pool managers send invitations only to a subset of miners based on a non-uniform probability distribution defined by the miners’ reputation values. We show that by using our proposed solution concept, honest mining becomes Nash Equilibrium in our setting. In other words, it will not be in the best interest of the miners to employ dishonest mining strategies even by gaining a short-term utility. This is due to the consideration of a long-term utility in our model and its impact on the miners’ utilities overtime.
CITATION STYLE
Nojoumian, M., Golchubian, A., Njilla, L., Kwiat, K., & Kamhoua, C. (2019). Incentivizing blockchain miners to avoid dishonest mining strategies by a reputation-based paradigm. In Advances in Intelligent Systems and Computing (Vol. 857, pp. 1118–1134). Springer Verlag. https://doi.org/10.1007/978-3-030-01177-2_81
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