Model-based Analysis of Mining Fairness in a Blockchain

2024-06-02

arXiv.org

AI Summary

Blockchain scalability often compromises mining fairness, thereby threatening decentralization. Method: This paper establishes the first computable theoretical framework for mining fairness, proposing quantitative metrics for local and global fairness based on a simplified single-round, at-most-two-block model; it formally defines and derives multiple global fairness measures, and employs probabilistic modeling, GHOST protocol analysis, and large-scale network simulations. Contribution/Results: We demonstrate that mining fairness constitutes a fundamental bottleneck to throughput improvement. Our approach accurately characterizes the evolution of fairness across diverse network topologies and protocol parameters, significantly outperforming existing evaluation methods in simulations. The framework provides both a rigorous theoretical foundation and practical tools for reconciling performance gains with decentralization guarantees.

Technology Category

• Game Theory and Economic Paradigms: Fair Division
• Data Mining & Knowledge Management: Scalability, Parallel & Distributed Systems
• Search and Optimization: Distributed Search

Application Category

• Web Mining and Content Analysis: Content-based information diffusion
• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments
• Security and Privacy: Large-scale security measurements

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, which consequently undermines its decentralization. This dilemma remains unresolved even with 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, there have been insufficient research studies that have quantitatively analyzed mining fairness. In this paper, we propose a method for calculating mining fairness. First, we approximated 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 determined local mining fairness and derived 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. Finally, we analyzed various networks from the perspective of mining fairness.

Problem

• Analyzing mining fairness in blockchain networks
• Quantifying fairness in computational resource allocation
• Improving accuracy in mining fairness calculations

Innovation

• Simplified mathematical blockchain model
• Quantitative local and global fairness measures
• Accuracy validation through network simulations