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Tailstorm: A Secure and Fair Blockchain for Cash Transact...
Patrik Keller, Ben Glickenhaus, George Bissias, Gregory Griffith · 2023-06-21 · via cs.DC updates on arXiv.org

Proof-of-work (PoW) cryptocurrencies rely on a balance of security and fairness in order to maintain a sustainable ecosystem of miners and users. Users demand fast and consistent transaction confirmation, and in exchange drive the adoption and valuation of the cryptocurrency. Miners provide the confirmations, however, they primarily seek rewards. In unfair systems, miners can amplify their rewards by consolidating mining power. Centralization however, undermines the security guarantees of the system and might discourage users. In this paper we present Tailstorm, a cryptocurrency that strikes this balance. Tailstorm merges multiple recent protocol improvements addressing security, confirmation latency, and throughput with a novel incentive mechanism improving fairness. We implement a parallel proof-of-work consensus mechanism with $k$ PoWs per block to obtain state-of-the-art consistency guarantees. Inspired by Bobtail and Storm, we structure the individual PoWs in a tree which, by including a list of transactions with each PoW, reduces confirmation latency and improves throughput. Our proposed incentive mechanism discounts rewards based on the depth of this tree. Thereby, it effectively punishes information withholding, the core attack strategy used to reap an unfair share of rewards. We back our claims with a comprehensive analysis. We present a generic system model which allows us to specify Bitcoin, $B_k$, and Tailstorm from a joint set of assumptions. We provide an analytical bound for the fairness of Tailstorm and Bitcoin in honest networks and we confirm the results through simulation. We evaluate the effectiveness of dishonest behaviour through reinforcement learning. Our attack search reproduces known optimal strategies against Bitcoin, uncovers new ones against $B_k$, and confirms that Tailstorm's reward discounting makes it more resilient to incentive layer attacks.