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Framework and algorithms for identifying honest blocks in blockchain

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  • Xu Wang
  • Guohua Gan
  • Ling-Yun Wu

Abstract

Blockchain technology gains more and more attention in the past decades and has been applied in many areas. The main bottleneck for the development and application of blockchain is its limited scalability. Blockchain with directed acyclic graph structure (BlockDAG) is proposed in order to alleviate the scalability problem. One of the key technical problems in BlockDAG is the identification of honest blocks which are very important for establishing a stable and invulnerable total order of all the blocks. The stability and security of BlockDAG largely depends on the precision of honest block identification. This paper presents a novel universal framework based on graph theory, called MaxCord, for identifying the honest blocks in BlockDAG. By introducing the concept of discord, the honest block identification is modelled as a generalized maximum independent set problem. Several algorithms are developed, including exact, greedy and iterative filtering algorithms. The extensive comparisons between proposed algorithms and the existing method were conducted on the simulated BlockDAG data to show that the proposed iterative filtering algorithm identifies the honest blocks both efficiently and effectively. The proposed MaxCord framework and algorithms can set the solid foundation for the BlockDAG technology.

Suggested Citation

  • Xu Wang & Guohua Gan & Ling-Yun Wu, 2020. "Framework and algorithms for identifying honest blocks in blockchain," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-14, January.
    • Handle: RePEc:plo:pone00:0227531
    DOI: 10.1371/journal.pone.0227531
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    References listed on IDEAS

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    1. Manuel Laguna & Thomas A. Feo & Hal C. Elrod, 1994. "A Greedy Randomized Adaptive Search Procedure for the Two-Partition Problem," Operations Research, INFORMS, vol. 42(4), pages 677-687, August.
    2. Bruno Biais & Christophe Bisière & Matthieu Bouvard & Catherine Casamatta, 2019. "The Blockchain Folk Theorem," Review of Financial Studies, Society for Financial Studies, vol. 32(5), pages 1662-1715.
    3. Young Bin Kim & Jurim Lee & Nuri Park & Jaegul Choo & Jong-Hyun Kim & Chang Hun Kim, 2017. "When Bitcoin encounters information in an online forum: Using text mining to analyse user opinions and predict value fluctuation," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-14, May.
    4. Thomas A. Feo & Mauricio G. C. Resende & Stuart H. Smith, 1994. "A Greedy Randomized Adaptive Search Procedure for Maximum Independent Set," Operations Research, INFORMS, vol. 42(5), pages 860-878, October.
    5. Péter L Juhász & József Stéger & Dániel Kondor & Gábor Vattay, 2018. "A Bayesian approach to identify Bitcoin users," PLOS ONE, Public Library of Science, vol. 13(12), pages 1-21, December.
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    Cited by:

    1. E. Sandeep Kumar, 2022. "Preserving Privacy in Ethereum Blockchain," Annals of Data Science, Springer, vol. 9(4), pages 675-693, August.

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