Literature DB >> 31914455

Framework and algorithms for identifying honest blocks in blockchain.

Xu Wang1,2,3, Guohua Gan3,4,5, Ling-Yun Wu1,2,3.   

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.

Entities:  

Year:  2020        PMID: 31914455      PMCID: PMC6949114          DOI: 10.1371/journal.pone.0227531

Source DB:  PubMed          Journal:  PLoS One        ISSN: 1932-6203            Impact factor:   3.240


  4 in total

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Authors:  Ladislav Kristoufek
Journal:  PLoS One       Date:  2015-04-15       Impact factor: 3.240

2.  When Bitcoin encounters information in an online forum: Using text mining to analyse user opinions and predict value fluctuation.

Authors:  Young Bin Kim; Jurim Lee; Nuri Park; Jaegul Choo; Jong-Hyun Kim; Chang Hun Kim
Journal:  PLoS One       Date:  2017-05-12       Impact factor: 3.240

3.  A Bayesian approach to identify Bitcoin users.

Authors:  Péter L Juhász; József Stéger; Dániel Kondor; Gábor Vattay
Journal:  PLoS One       Date:  2018-12-13       Impact factor: 3.240

Review 4.  Where Is Current Research on Blockchain Technology?-A Systematic Review.

Authors:  Jesse Yli-Huumo; Deokyoon Ko; Sujin Choi; Sooyong Park; Kari Smolander
Journal:  PLoS One       Date:  2016-10-03       Impact factor: 3.240

  4 in total

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