Analysis of variance of graph-clique mining for scalable proof of work

Hiroaki Anada, Tomohiro Matsushima, Chunhua Su, Weizhi Meng, Junpei Kawamoto, Samiran Bag, Kouichi Sakurai

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Recently, Bitcoin is becoming one of the most popular decentralized cryptographic currency technologies, and Bitcoin mining is a process of adding transaction records to Bitcoin’s public ledger of past transactions or blockchain. To obtain a bitcoin, the mining process involves compiling recent transactions into blocks and trying to solve a computationally difficult puzzle, e.g., proof of work puzzle. A proof of work allows miners the ability to quantify how much work a given proof contains. Basically, the required time for mining is decided in advance, but problems will occur if the value is large for dispersion. In this paper, we first accept that the required time between consecutive blocks follows the exponential distribution. That is, the variance is stable as long as the expected time is fixed. Then, we focus on the graph clique mining technique proposed by the literature, like Tromp (BITCOIN 2015) and Bag-Ruj-Sakurai (Inscrypt 2015), which is based on a computational difficulty problem of searching cliques of undirected graphs, where a clique is a subset of vertices. In particular, when the clique size is two, graph clique mining can be used to gain Bitcoins. The previous work also claimed that if the clique size is parameterized and increased, even if the expected time is fixed, the variance would not be stable. However, no qualitative or quantitative results were given to support their claim. Motivated by this issue, in this work, we propose a simple search algorithm for graph cliques mining, and perform a small scale evaluation on Bitcoin and Graph cliques’s solo mining to investigate the variance issue.

Original languageEnglish
Title of host publicationInformation Security and Cryptology - 14th International Conference, Inscrypt 2018, Revised Selected Papers
EditorsMoti Yung, Xinyi Huang, Fuchun Guo
PublisherSpringer Verlag
Pages101-114
Number of pages14
ISBN (Print)9783030142339
DOIs
Publication statusPublished - 2019
Event14th International Conference on Information Security and Cryptology, Inscrypt 2018 - Fuzhou, China
Duration: Dec 14 2018Dec 17 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11449 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference14th International Conference on Information Security and Cryptology, Inscrypt 2018
CountryChina
CityFuzhou
Period12/14/1812/17/18

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Anada, H., Matsushima, T., Su, C., Meng, W., Kawamoto, J., Bag, S., & Sakurai, K. (2019). Analysis of variance of graph-clique mining for scalable proof of work. In M. Yung, X. Huang, & F. Guo (Eds.), Information Security and Cryptology - 14th International Conference, Inscrypt 2018, Revised Selected Papers (pp. 101-114). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11449 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-14234-6_6