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Framework and algorithms for identifying honest blocks in blockchain.

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This study introduces MaxCord, a novel framework for identifying honest blocks in Blockchain with Directed Acyclic Graph (BlockDAG) structures. MaxCord enhances BlockDAG stability and security by improving honest block identification efficiency and effectiveness.

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Area of Science:

  • Computer Science
  • Distributed Systems
  • Graph Theory

Background:

  • Blockchain technology faces scalability limitations, hindering widespread application.
  • Blockchain with Directed Acyclic Graph (BlockDAG) structures offer a potential solution to scalability issues.
  • Accurate identification of honest blocks is crucial for BlockDAG stability and security.

Purpose of the Study:

  • To present a novel universal framework, MaxCord, for identifying honest blocks in BlockDAG.
  • To model honest block identification as a generalized maximum independent set problem using the concept of discord.
  • To develop and evaluate algorithms for efficient and effective honest block identification.

Main Methods:

  • Developed a graph theory-based framework named MaxCord.
  • Introduced the concept of 'discord' to model honest block identification.
  • Created exact, greedy, and iterative filtering algorithms for honest block identification.

Main Results:

  • The proposed MaxCord framework effectively identifies honest blocks in BlockDAG.
  • Extensive comparisons on simulated BlockDAG data show the iterative filtering algorithm is efficient and effective.
  • The MaxCord framework provides a robust foundation for BlockDAG technology.

Conclusions:

  • MaxCord offers a significant advancement in BlockDAG honest block identification.
  • The developed algorithms, particularly the iterative filtering approach, demonstrate superior performance.
  • This work lays the groundwork for more stable and secure BlockDAG implementations.