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Insightful Mining Equilibria

  • Mengqian Zhang
  • , Yuhao Li
  • , Jichen Li
  • , Chaozhe Kong
  • , Xiaotie Deng*
  • *Corresponding author for this work

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

Abstract

The selfish mining attack, arguably the most famous game-theoretic attack in blockchain, indicates that the Bitcoin protocol is not incentive-compatible. Most subsequent works mainly focus on strengthening the selfish mining strategy, thus enabling a single strategic agent more likely to deviate. In sharp contrast, little attention has been paid to the resistant behavior against the selfish mining attack, let alone further equilibrium analysis for miners and mining pools in the blockchain as a multi-agent system. In this paper, first, we propose a novel strategy called insightful mining to counteract the selfish mining attack. By infiltrating an undercover miner into the selfish pool, the insightful pool could acquire the number of its hidden blocks. We prove that, with this extra insight, the utility of the insightful pool is strictly greater than the selfish pool’s when they have the same mining power. Then we investigate the mining game where all pools can choose to be honest or take the insightful mining strategy. We characterize the Nash equilibrium of such a game and derive three corollaries: (a) each mining game has a pure Nash equilibrium; (b) there are at most two insightful pools under some equilibrium no matter how the mining power is distributed; (c) honest mining is a Nash equilibrium if the largest mining pool has a fraction of mining power no more than 1/3. Our work explores, for the first time, the idea of spying in the selfish mining attack, which might shed new light on researchers in the field. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Original languageEnglish
Title of host publicationWeb and Internet Economics
Subtitle of host publication18th International Conference, WINE 2022, Proceedings
EditorsKristoffer Arnsfelt Hansen, Tracy Xiao Liu, Azarakhsh Malekian
PublisherSpringer, Cham
Pages21-37
Number of pages17
Edition1
ISBN (Electronic)978-3-031-22832-2
ISBN (Print)978-3-031-22831-5
DOIs
Publication statusPublished - Dec 2022
Externally publishedYes
Event18th International Conference on Web and Internet Economics (WINE 2022) - Troy, United States
Duration: 12 Dec 202215 Dec 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13778 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference18th International Conference on Web and Internet Economics (WINE 2022)
PlaceUnited States
CityTroy
Period12/12/2215/12/22

Funding

Keywords: Blockchain · Selfish mining · Markov process · Insightful mining · Mining game This work was supported by Science and Technology Innovation 2030 - “New Generation Artificial Intelligence” Major Project No. 2018AAA0100901. This work has been performed with support from the Algorand Foundation Grants Program. Y. Li—Supported by NSF grants CCF-1563155, CCF-1703925, IIS-1838154, CCF-2106429 and CCF-2107187.

Research Keywords

  • Blockchain
  • Insightful mining
  • Markov process
  • Mining game
  • Selfish mining

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