Active Defense Analysis of Blockchain Forking through the Spatial-Temporal Lens
Shengling Wang, Ying Wang, Hongwei Shi, and Qin Hu

TL;DR
This paper introduces a novel spatial-temporal model for blockchain forking analysis, emphasizing active defense strategies by reducing long-range links to prevent forking, supported by theoretical and simulation results.
Contribution
It is the first to analyze blockchain forking from a spatial-temporal perspective and proposes active defense mechanisms by controlling network topology.
Findings
Shrinking long-range links can eliminate forking.
Spatial-temporal modeling provides new insights into forking behavior.
Active defense strategies are effective in preventing forking.
Abstract
Forking breaches the security and performance of blockchain as it is symptomatic of distributed consensus, spurring wide interest in analyzing and resolving it. The state-of-the-art works can be categorized into two kinds: experiment-based and model-based. However, the former falls short in exclusiveness since the derived observations are scenario-specific. Hence, it is problematic to abstractly reveal the crystal-clear forking laws. Besides, the models established in the latter are spatiality-free, which totally overlook the fact that forking is essentially an undesirable result under a given topology. Moreover, few of the ongoing studies have yielded to the active defense mechanisms but only recognized forking passively, which impedes forking prevention and cannot deter it at the source. In this paper, we fill the gap by carrying out the active defense analysis of blockchain forking…
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Taxonomy
TopicsBlockchain Technology Applications and Security · Peer-to-Peer Network Technologies · Complex Network Analysis Techniques
