TY - GEN
T1 - Selfish Mining Re-Examined
AU - Negy, Kevin Alarcón
AU - Rizun, Peter R.
AU - Sirer, Emin Gün
N1 - Publisher Copyright:
© 2020, International Financial Cryptography Association.
PY - 2020
Y1 - 2020
N2 - Six years after the introduction of selfish mining, its counterintuitive findings continue to create confusion. In this paper, we comprehensively address one particular source of misunderstandings, related to difficulty adjustments. We first present a novel, modified selfish mining strategy, called intermittent selfish mining, that, perplexingly, is more profitable than honest mining even when the attacker performs no selfish mining after a difficulty adjustment. Simulations show that even in the most conservative scenario an intermittent selfish miner above 37% hash power earns more coins per time unit than their fair share. We then broadly examine the profitability of selfish mining under several difficulty adjustment algorithms (DAAs) used in popular cryptocurrencies. We present a taxonomy of popular difficulty adjustment algorithms, quantify the effects of algorithmic choices on hash fluctuations, and show how resistant different DAA families are to selfish mining.
AB - Six years after the introduction of selfish mining, its counterintuitive findings continue to create confusion. In this paper, we comprehensively address one particular source of misunderstandings, related to difficulty adjustments. We first present a novel, modified selfish mining strategy, called intermittent selfish mining, that, perplexingly, is more profitable than honest mining even when the attacker performs no selfish mining after a difficulty adjustment. Simulations show that even in the most conservative scenario an intermittent selfish miner above 37% hash power earns more coins per time unit than their fair share. We then broadly examine the profitability of selfish mining under several difficulty adjustment algorithms (DAAs) used in popular cryptocurrencies. We present a taxonomy of popular difficulty adjustment algorithms, quantify the effects of algorithmic choices on hash fluctuations, and show how resistant different DAA families are to selfish mining.
UR - https://www.scopus.com/pages/publications/85089224024
UR - https://www.scopus.com/inward/citedby.url?scp=85089224024&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-51280-4_5
DO - 10.1007/978-3-030-51280-4_5
M3 - Conference contribution
AN - SCOPUS:85089224024
SN - 9783030512798
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 61
EP - 78
BT - Financial Cryptography and Data Security - 24th International Conference, FC 2020, Revised Selected Papers
A2 - Bonneau, Joseph
A2 - Heninger, Nadia
PB - Springer
T2 - 24th International Conference on Financial Cryptography and Data Security, FC 2020
Y2 - 10 February 2020 through 14 February 2020
ER -