On the Profitability of Selfish Mining Against Multiple Difficulty Adjustment Algorithms

Published

January 29, 2020

Author(s)

Michael S. Davidson, Tyler J. Diamond

Abstract

The selfish mining attack allows cryptocurrency miners to mine more than their "fair share" of blocks, stealing revenue from other miners while reducing the overall security of payments. This malicious strategy has been extensively studied in Bitcoin, but far less attention has been paid to how the strategy may impact other cryptocurrencies. Because selfish mining is an attack against the difficulty adjustment algorithm (DAA) of a cryptocurrency, it may have a different effect when used on coins with different DAAs. In this work, we study the degree to which selfish mining can increase the revenue of miners for a wider variety of cryptocurrencies than have been studied before, including Bitcoin, Litecoin, Bitcoin Cash, Dash, Monero, and zCash. To do so, we generalize the selfish mining strategy to blockchains with variable difficulty, and use simulations to measure how profitable the strategy is. We find that the other cryptocurrencies under consideration are far more susceptible to selfish mining than Bitcoin is, and that the strategy is profitable for miners with a lower hash rate. We also show that by dishonestly reporting block timestamps, selfish miners can generate enormously disproportionate revenues up to 2.5 times larger than they would through honest mining for some DAAs. For each DAA, we consider what happens when parameters are changed, and suggest parameter sets that would improve the algorithm's resilience against selfish mining.

Citation

Cryptology ePrint Archive

Pub Weblink

https://eprint.iacr.org/2020/094

Pub Type

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Keywords

Bitcoin, Cryptocurrency, Proof of Work, Selfish Mining, Difficulty Adjustment Algorithm, Blockchain

Citation

Davidson, M. and Diamond, T. (2020), On the Profitability of Selfish Mining Against Multiple Difficulty Adjustment Algorithms, Cryptology ePrint Archive, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=929287, https://eprint.iacr.org/2020/094 (Accessed April 24, 2024)