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Continuous improvement of script-driven verifiable random functions for reducing computing power in blockchain consensus protocols

delete2021-10-06
delete8
PRE
AI
G
Guanglai Guo
朱岩 (Yan Zhu) *
E
E Chen
G
Guizhen Zhu
D
Di Ma
W
William Cheng‐Chung Chu
DOI:10.1007/s12083-021-01243-xdelete
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Abstract

Abstract

En 中文
In order to solve the problem of low efficiency and high energy consumption of the Proof-of-Work (PoW) consensus protocol in blockchain within a peer-to-peer network, some new protocols based on Verifiable Random Function (VRF) have emerged recently. However, these VRF-based consensus protocols do not actually give a concrete and efficient VRF construction. In view of this, we present three simple and practical VRF constructions from the RSA hardness assumption, the Decisional Diffie-Hellman (DDH) assumption and the Leftover Hash Lemma (LHL) respectively, the output size of which is continuously reduced for the design of efficient consensus protocol in blockchain. We also give a complete security analysis of our VRF constructions. Furthermore, we show a specific application of our VRF constructions in the famous Algorand consensus protocol. We illustrate a general approach to integrate our VRF constructions with block structure in blockchain. Comparing with PoW-based mining, we demonstrate the detailed process of VRF-based consensus protocol. Meanwhile, three new opcodes are designed for the scripting system in blockchain to develop a script pair, scriptProof and scriptHash, which provides secure and efficient block verification. Finally, we evaluate the performance of our VRF constructions in terms of storage and computational overheads, and the experimental evaluation results show our VRF constructions can significantly reduce the computing power of consensus protocol in blockchain.
Keywords:
Verifiable random function
Script instruction
Consensus protocol
Blockchain
Leftover hash lemma
Performance
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Peer-to-Peer Networking and Applications cover
Peer-to-Peer Networking and Applications
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Tunghai University
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University of Michigan
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