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Blockchain Mining With Multiple Selfish Miners

delete2023-01-01
delete6
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OA
AI
Q
Qianlan Bai
徐
徐跃东 (Yuedong Xu) *
N
Nianyi Liu
X
Xin Wang
DOI:10.1109/TIFS.2023.3275736delete
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摘要

摘要

En 中文
This paper studies a fundamental problem regarding the security of blockchain PoW consensus on how the existence of multiple misbehaving miners influences the profitability of selfish mining. Each selfish miner maintains a private chain and makes it public opportunistically for acquiring more rewards incommensurate to his Hash power. We first establish a general Markov chain model to characterize the state transition of public and private chains for Basic Selfish Mining (BSM), and derive the stationary profitable threshold of Hash power in closed form. It reduces from 25% for a single attacker to below 21.48% for two symmetric attackers theoretically, and further reduces to around 10% with eight symmetric attackers experimentally. We next explore the profitable threshold when one of the attackers performs strategic mining based on Partially Observable Markov Decision Process (POMDP) that only half of the attributes pertinent to a mining state are observable to him. An online algorithm is presented to compute the nearly optimal policy efficiently despite the large state space and high dimensional belief space. The profitable threshold is much lower for the strategic attacker. Last, we formulate a simple model of absolute mining revenue that yields an interesting observation: selfish mining is never profitable at the first difficulty adjustment period, but relies on the reimbursement of stationary selfish mining gains in future periods. The delay till being profitable of an attacker increases with the decrease of his Hash power, making blockchain miners more cautious about performing selfish mining.
Keyword:
Blockchains
Markov processes
Computational modeling
Bitcoin
Profitability
Delays
Publishing
Proof-of-work
selfish mining
profitability
Markov chain
partially observable MDP

期刊

IEEE Transactions on Information Forensics and Security 封面图
IEEE Transactions on Information Forensics and Security
IF:
8
论文数:
5.3K
被引数:
2.3W

机构

F
fudan university
学者数:
11.8W
论文数: 7.7W
被引数: 121
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