计算机应用 ›› 2019, Vol. 39 ›› Issue (3): 918-923.DOI: 10.11772/j.issn.1001-9081.2018071619

• 应用前沿、交叉与综合 • 上一篇    下一篇

基于自适应零行列式策略的区块链矿池合作演化方法

范丽, 郑红, 黄建华, 李忠诚, 江亚慧   

  1. 华东理工大学 信息科学与工程学院, 上海 200237
  • 收稿日期:2018-08-03 修回日期:2018-09-12 出版日期:2019-03-10 发布日期:2019-03-11
  • 通讯作者: 黄建华
  • 作者简介:范丽(1993-),女,山东滕州人,硕士研究生,主要研究方向:区块链、信息安全;郑红(1973-),女,上海人,副教授,博士,主要研究方向:普适计算、系统建模和分析;黄建华(1963-),男,上海人,副教授,博士,CCF会员,主要研究方向:计算机网络、无线传感网、区块链、信息安全;李忠诚(1994-),男,安徽蚌埠人,硕士研究生,主要研究方向:区块链、信息安全;江亚慧(1994-),女,江苏泰州人,硕士研究生,主要研究方向:区块链、信息安全。
  • 基金资助:

    国家自然科学基金资助项目(61473118)。

Cooperative evolution method for blockchain mining pool based on adaptive zero-determinant strategy

FAN Li, ZHENG Hong, HUANG Jianhua, LI Zhongcheng, JIANG Yahui   

  1. School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, China
  • Received:2018-08-03 Revised:2018-09-12 Online:2019-03-10 Published:2019-03-11
  • Supported by:

    This work is partially supported by the National Natural Science Foundation of China (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

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.

Key words: bitcoin, temporal difference learning method, adaptive strategy, zero-determinant strategy, Decision Making Process (DMP)

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