Sensitivity-Based Optimization for Blockchain Selfish Mining

TL;DR

This paper introduces a sensitivity-based optimization approach to determine optimal dynamic policies for selfish mining in blockchain, analyzing a Markov process to enhance understanding of attack strategies.

Contribution

It presents a novel dynamic decision method using sensitivity-based optimization to identify optimal blockchain-pegged policies for dishonest mining pools.

Findings

Proves the structure of the optimal blockchain-pegged policy.

Analyzes the monotonicity and optimality of long-run profits.

Provides a methodology for dynamic decision-making in selfish mining attacks.

Abstract

In this paper, we provide a novel dynamic decision method of blockchain selfish mining by applying the sensitivity-based optimization theory. Our aim is to find the optimal dynamic blockchain-pegged policy of the dishonest mining pool. To study the selfish mining attacks, two mining pools is designed by means of different competitive criterions, where the honest mining pool follows a two-block leading competitive criterion, while the dishonest mining pool follows a modification of two-block leading competitive criterion through using a blockchain-pegged policy. To find the optimal blockchain-pegged policy, we set up a policy-based continuous-time Markov process and analyze some key factors. Based on this, we discuss monotonicity and optimality of the long-run average profit with respect to the blockchain-pegged reward and prove the structure of the optimal blockchain-pegged policy. We hope the methodology and results derived in this paper can shed light on the dynamic decision research on the selfish mining attacks of blockchain selfish mining.