Academic Journal
Verifier's Dilemma in Proof-of-Work Public Blockchains: A Quantitative Analysis.
| Τίτλος: | Verifier's Dilemma in Proof-of-Work Public Blockchains: A Quantitative Analysis. |
|---|---|
| Συγγραφείς: | SMUSEVA, DARIA, MARIN, ANDREA, ROSSI, SABINA, VAN MOORSEL, AAD |
| Πηγή: | ACM Transactions on Modeling & Computer Simulation; Apr2025, Vol. 35 Issue 2, p1-24, 24p |
| Θεματικοί όροι: | Markov processes, Stochastic processes, Bitcoin, Miners, Dilemma, Blockchains |
| Περίληψη: | A blockchain is an immutable ledger driven by a distributed consensus protocol. In public blockchains, such as Bitcoin and Ethereum Classic, consensus is established through a computational effort called Proof-of-Work (PoW). Special users called miners contribute to the PoW in exchange for a fee and also verify the data stored in blocks mined by the other miners. Here is where the Verifier's Dilemma emerges. Verification of blocks does not receive a reward, and to maximise their profits, miners may be incentivised to forego verifying blocks and to only invest their resources in PoW. In this article, we study the Verifier's Dilemma and a possible countermeasure consisting of the injection of invalid blocks using a quantitative model based on Markovian process algebra. To avoid the state space explosion problem, we study the underlying Markov chain by using a lumping that allows us to derive closed-form solutions for interesting performance indices. The analysis demonstrates the circumstances under which non-verifying miners gain fees higher than those of verifying miners. The model also allows us to derive the optimal rate at which invalid blocks must be injected so that skipping the verifying phase becomes economically disadvantageous whereas the throughput of the blockchain is only minimally reduced. The impact on miners' rewards and overall performance is also assessed. [ABSTRACT FROM AUTHOR] |
| Copyright of ACM Transactions on Modeling & Computer Simulation is the property of Association for Computing Machinery and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
καταχωρήστε σχόλιο πρώτοι!