Research Article
Selfish Mining in Public Blockchains: A Quantitative Analysis
@INPROCEEDINGS{10.1007/978-3-031-48885-6_2, author={Daria Smuseva and Andrea Marin and Sabina Rossi}, title={Selfish Mining in Public Blockchains: A Quantitative Analysis}, proceedings={Performance Evaluation Methodologies and Tools. 16th EAI International Conference, VALUETOOLS 2023, Crete, Greece, September 6--7, 2023, Proceedings}, proceedings_a={VALUETOOLS}, year={2024}, month={1}, keywords={Blockchain security Stochastic process algebra Selfish mining attack}, doi={10.1007/978-3-031-48885-6_2} }- Daria Smuseva
Andrea Marin
Sabina Rossi
Year: 2024
Selfish Mining in Public Blockchains: A Quantitative Analysis
VALUETOOLS
Springer
DOI: 10.1007/978-3-031-48885-6_2
Abstract
Blockchains are digital ledgers of transactions that aim to be decentralized, secure, and tamper-proof. To achieve this goal, they rely on a consensus algorithm, with the most well-known being the proof-of-work (PoW) algorithm. In PoW, a group of specialized users known as miners invest a significant amount of energy to secure the blockchain ledger. Miners are incentivized to participate in the network through the potential rewards they can earn, which are based on the number of blocks they are able to consolidate and add to the chain. An important characteristic of the PoW algorithm is that miners’ rewards must be statistically proportional to the amount of computational power (and hence energy) invested in this process. In this work, we study the selfish miner attack by means of a stochastic model based on a quantitative process algebra. When a successful attack occurs, a miner or mining pool is able to receive more rewards than they should, at the expense of other miners. The model analysis allows us to derive the conditions under which the attack becomes convenient for the miners.
