Fully Automated Selfish Mining Analysis in Efficient Proof Systems Blockchains
Krishnendu Chatterjee, Amirali Ebrahimzadeh, Mehrdad Karrabi,, Krzysztof Pietrzak, Michelle Yeo, {\DJ}or{\dj}e \v{Z}ikeli\'c

TL;DR
This paper introduces a fully automated method to analyze and optimize selfish mining strategies in blockchain systems that use efficient proof mechanisms, providing formal guarantees and outperforming baseline attacks.
Contribution
It models selfish mining as a Markov decision process and develops an automated analysis procedure to compute near-optimal attack strategies with formal correctness guarantees.
Findings
The proposed attack achieves higher expected revenue than baseline strategies.
The analysis provides an $oldsymbol{ extit{ extepsilon}}$-tight lower bound on optimal revenue.
The method is fully automated and offers formal correctness guarantees.
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 -tight lower bound on the optimal expected relative revenue in the MDP and a strategy that achieves this -tight lower bound, where may be any specified precision. Our analysis is fully automated and provides formal guarantees on the correctness. We evaluate our selfish mining attack and…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
