Mathematics (Nov 2023)
Less Is More: Understanding Network Bias in Proof-of-Work Blockchains
Abstract
Blockchains are becoming increasingly important in today’s Internet, enabling large-scale decentralized applications with strong security and transparency properties. In a blockchain system, participants maintain and update the server-side state of an application by appending data as blocks onto an immutable, distributed ledger through a consensus protocol within a peer-to-peer network. There has been a significant increase in profit in mining blocks. For instance, Bitcoin miners currently receive over USD 200,000 per mined block. An essential determinant of these rewards is the time it takes to disseminate newly mined blocks across the network. This paper addresses the challenge of optimizing mining rewards by exploring topology design in a wide-area blockchain network utilizing a Proof-of-Work consensus protocol. We show that under low block times, the geographical location of a miner critically impacts the number of successful blocks mined by the miner. We also show that a miner may improve its success rate by increasing its connectivity to the network. However, contrary to the general wisdom that a faster network is always better for a miner, we show that increasing network connectivity (e.g., by adding more neighbors) is beneficial to a miner only up to a point after which the miner’s rewards degrade. This is because when a miner improves its connectivity, it inadvertently also aids other miners in increasing their connectivity. We also present a network-level collusion attack in which a miner can increase its block success rate by becoming part of a tightly connected cluster. Here too, we observe that the mining gains obtained increase with cluster size only up to a point, and decrease thereafter. Our findings highlight that the network topology is a key variable affecting miner performance in PoW blockchains that must not be overlooked. We demonstrate our observations via detailed simulations modeled using real-world measurement data.
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