FastDCV: An efficient cross-chain data consistency verification scheme supporting batch processing

Yuwei Xu, Junyu Zeng, Shengjiang Dai, Qiao Xiang, Jun Tao, Guang Cheng

Abstract: The consortium blockchain, due to its inherent characteristics, has been widely adopted across various industries. As the number of consortium blockchains grows, data silos are becoming a bigger issue, blocking data sharing between industries. Current cross-chain technologies solve asset transfer issues with complicated transactions, but they do not efficiently transmit data. While some researchers have shifted their focus to cross-chain data consistency, current solutions suffer from two major shortcomings. First, they predominantly use Merkle tree structures, which result in lower efficiency when constructing the tree. Second, these solutions focus solely on scenarios pertaining to an individual request within a request chain, rather than addressing multiple requests simultaneously. As a result, their verification efficiency is poor when handling a large number of requests. To address these issues, we propose FastDCV, an efficient cross-chain data consistency verification scheme that supports batch verification. It introduces a Right-Leaning (RL) Merkle tree structure and a corresponding auxiliary verification table. The RL Merkle tree enhances efficiency, and the auxiliary verification table empowers FastDCV to facilitate batch auditing, delivering excellent performance when handling a high volume of requests. Finally, we developed a prototype system and evaluated its performance. The results demonstrate that FastDCV can perform efficient verification when facing a large number of requests, and has better performance than existing schemes.

External IDs:dblp:journals/ppna/XuZDXTC25