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基于自适应零行列式策略的区块链矿池合作演化方法
引用本文:范丽,郑红,黄建华,李忠诚,江亚慧.基于自适应零行列式策略的区块链矿池合作演化方法[J].计算机应用,2019,39(3):918-923.
作者姓名:范丽  郑红  黄建华  李忠诚  江亚慧
作者单位:华东理工大学 信息科学与工程学院,上海,200237;华东理工大学 信息科学与工程学院,上海,200237;华东理工大学 信息科学与工程学院,上海,200237;华东理工大学 信息科学与工程学院,上海,200237;华东理工大学 信息科学与工程学院,上海,200237
基金项目:国家自然科学基金资助项目(61473118)。
摘    要:矿工加入矿池是目前比特币挖矿最常见的方式。然而,比特币系统中存在矿池互相渗透攻击的现象,这将导致被攻击矿池的矿工收益减少,发起攻击的矿池算力降低,从而造成比特币系统的整体算力减小。针对矿池之间互相攻击,不合作挖矿的问题,提出自适应零行列式策略(AZD),采取"比较预期合作收益与背叛收益,选择促进高收益的策略"的思想促进矿池合作。首先,通过结合时序差分增强算法与零行列式策略的方法预测下一轮合作收益与背叛收益;其次,通过决策过程(DMP)选择策略进一步改变下一轮的合作概率和背叛概率;最后,通过迭代执行自适应零行列式策略,达到网络中矿池均互相合作、积极挖矿的目的。实验模拟表明,AZD策略与自适应策略相比,合作概率收敛为1的速度提高了36.54%;与零行列式策略相比,稳定度提高了50%。这个结果表明AZD策略能够有效促进矿工合作,提高合作收敛速率,保证矿池的稳定收益。

关 键 词:比特币  时序差分增强算法  自适应策略方法  零行列式策略  决策过程
收稿时间:2018-08-03
修稿时间:2018-09-12

Cooperative evolution method for blockchain mining pool based on adaptive zero-determinant strategy
FAN Li,ZHENG Hong,HUANG Jianhua,LI Zhongcheng,JIANG Yahui.Cooperative evolution method for blockchain mining pool based on adaptive zero-determinant strategy[J].journal of Computer Applications,2019,39(3):918-923.
Authors:FAN Li  ZHENG Hong  HUANG Jianhua  LI Zhongcheng  JIANG Yahui
Affiliation:School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, China
Abstract:At present, the most common way for bitcoin mining is miners joining in a pool. However, there is a phenomenon that the mining pools penetrate each other, which will result in a decrease in the miners' income of the attacked pools, and a reduction in computing power of the attacking pools. Therefore, the overall computing power of the bitcoin system is reduced. Aiming at the problem of mutual attack and non-cooperative mining between mining pools, an Adaptive Zero-Determinant strategy (AZD) was proposed to promote the cooperation of miners. The strategy adopted the idea of comparing expected payoff with cooperation and defection in the next round then choosing a strategy with high payoff. Firstly, miners' payoff in the next round under two situations could be predicted by the combination of Temporal Difference Learning Method (TD(λ)) and Zero-Determinant strategy (ZD). Secondly, by comparing the cooperation payoff with defection payoff in the next round, a more favorable strategy was chosen for miners by Decision Making Process (DMP), so the cooperation probability and defection probability in the next round were changed correspondingly. Finally, through the iterative implementation of AZD strategy, the ming pools in the network would cooperate with each other and mine actively. Simulation results show that compared with adaptive strategy, AZD strategy increases the speed of converging cooperation probability to 1 by 36.54%, compared with ZD strategy, it improves the stability by 50%. This result indicates that AZD strategy can effectively promote the cooperation of miners, improve the convergence rate of cooperation and ensure the stable income of mining pools.
Keywords:bitcoin                                                                                                                        temporal difference learning method                                                                                                                        adaptive strategy                                                                                                                        zero-determinant strategy                                                                                                                        Decision Making Process (DMP)
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