Model-Based Calculation Method of Mining Fairness in Blockchain

TL;DR

This paper introduces a mathematical model to accurately evaluate mining fairness in blockchain systems, explicitly considering blockchain forks and their impact on reward distribution, thus aiding in balancing scalability and decentralization.

Contribution

It proposes a novel calculation method that incorporates blockchain forks into mining fairness analysis, improving accuracy over existing approaches.

Findings

The model accurately measures local and global mining fairness.

Simulations show the method outperforms existing fairness evaluation techniques.

Provides a quantitative framework for assessing scalability and decentralization trade-offs.

Abstract

Mining fairness in blockchain refers to equality between the computational resources invested in mining and the block rewards received. There exists a dilemma wherein increasing the transaction processing capacity of a blockchain compromises mining fairness, thereby undermining its decentralization. This dilemma remains unresolved despite methods such as the greedy heaviest observed subtree (GHOST) protocol, indicating that mining fairness is an inherent bottleneck in the transaction processing capacity of the blockchain system. However, despite its significance, existing analyses neglect the impact of blockchain forks, resulting in imprecise evaluations and limited insights. To address this issue, we propose a method for calculating mining fairness that explicitly captures the influence of forks. First, we approximate a complex blockchain network using a simple mathematical model, assuming that no more than two blocks are generated per round. Within this model, we quantitatively determine local mining fairness and derive several measures of global mining fairness based on local mining fairness. Subsequently, we validated by blockchain network simulations that our calculation method computes mining fairness in networks much more accurately than existing methods. The proposed method facilitates a rigorous evaluation of trade-offs between scalability and decentralization by offering a clear, quantitative framework for measuring and comparing reward distribution among miners. Consequently, it is expected to provide valuable insights for future mining fairness research and the design of next-generation blockchain systems.