Minotaur: Multi-Resource Blockchain Consensus - Xuechao Wang

Xuechao Wang is a Ph.D. candidate in the Department of Electrical and Computer Engineering at the University of Illinois at Urbana-Champaign, working with Prof. Pramod Viswanath. His research interest is in decentralized consensus protocols (blockchains). He applies techniques from applied probability, combinatorics, and optimization to provide new algorithmic solutions and analyze the performance and security of new blockchain protocols.

Resource-based consensus is the backbone of permissionless distributed ledger systems. The security of such protocols relies fundamentally on the level of resources actively engaged in the system. The variety of different resources (and related proof protocols, sometimes referred to as PoX in the literature) raises the fundamental question of whether it is possible to utilize many of them in tandem and build multi-resource consensus protocols. The challenge in combining different resources is to achieve fungibility between them, in the sense that security would hold as long as the cumulative-adversarial power across all resources is bounded.

In this work, we put forth Minotaur, a multi-resource blockchain consensus protocol that combines proof of work (PoW) and proof-of-stake (PoS), and we prove it is optimally fungible. At the core of our design, Minotaur operates in epochs while continuously sampling the active computational power to provide a fair exchange between the two resources, work, and stake. Further, we demonstrate the ability of Minotaur to handle a higher degree of work fluctuation as compared to the Bitcoin blockchain; we also generalize Minotaurto any number of resources.

We demonstrate the simplicity of Minotaur via implementing a full-stack client in Rust. We use the client to test the robustness of Minotaur to variable mining power and combined work/stake attacks and demonstrate concrete empirical evidence towards the suitability of Minotaur to serve as the consensus layer of a real-world blockchain.

Related paper:
https://eprint.iacr.org/2022/104.pdf
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