Paper 2024/704
Fully Automated Selfish Mining Analysis in Efficient Proof Systems Blockchains
Abstract
We study selfish mining attacks in longest-chain blockchains like Bitcoin, but where the proof of work is replaced with efficient proof systems -- like proofs of stake or proofs of space -- and consider the problem of computing an optimal selfish mining attack which maximizes expected relative revenue of the adversary, thus minimizing the chain quality. To this end, we propose a novel selfish mining attack that aims to maximize this objective and formally model the attack as a Markov decision process (MDP). We then present a formal analysis procedure which computes an $\epsilon$-tight lower bound on the optimal expected relative revenue in the MDP and a strategy that achieves this $\epsilon$-tight lower bound, where $\epsilon>0$ may be any specified precision. Our analysis is fully automated and provides formal guarantees on the correctness. We evaluate our selfish mining attack and observe that it achieves superior expected relative revenue compared to two considered baselines. In concurrent work [Sarenche FC'24] does an automated analysis on selfish mining in predictable longest-chain blockchains based on efficient proof systems. Predictable means the randomness for the challenges is fixed for many blocks (as used e.g., in Ouroboros), while we consider unpredictable (Bitcoin-like) chains where the challenge is derived from the previous block.
Metadata
- Available format(s)
- Category
- Applications
- Publication info
- Published elsewhere. Minor revision. PODC
- Keywords
- BlockchainFormal MethodsEfficient Proof SystemsSelfish MiningMarkov Decision Process
- Contact author(s)
-
krishnendu chatterjee @ ist ac at
ebrahimzadeh amirali @ gmail com
mehrdad karrabi @ ist ac at
krzysztof pietrzak @ ist ac at
mxyeo @ nus edu sg
dzikelic @ smu edu sg - History
- 2024-05-10: approved
- 2024-05-07: received
- See all versions
- Short URL
- https://ia.cr/2024/704
- License
-
CC BY
BibTeX
@misc{cryptoeprint:2024/704, author = {Krishnendu Chatterjee and Amirali Ebrahim-Zadeh and Mehrdad Karrabi and Krzysztof Pietrzak and Michelle Yeo and Djordje Zikelic}, title = {Fully Automated Selfish Mining Analysis in Efficient Proof Systems Blockchains}, howpublished = {Cryptology {ePrint} Archive, Paper 2024/704}, year = {2024}, url = {https://eprint.iacr.org/2024/704} }