Towards Efficient Consistency Auditing of Dynamic Data in Cross-Chain Interaction

As blockchain technology matures and its adoption grows across multiple industries, there is a growing need to make the data stored on blockchains adaptable to prevent misuse. However, such modifications can lead to inconsistency when interacting with other blockchains, necessitating the preservation of dynamic data consistency during these cross-chain interactions. While ensuring consistency for static data is relatively straightforward, doing so for dynamic data with efficiency remains a significant challenge. In response, we propose an efficient dynamic cross-chain data consistency auditing model (EDCA), which artfully integrates an advanced gamma multi-signature approach (AGMS) with the designed dynamic Merkle hash tree (D-MHT) to facilitate effective auditing of the consistency for dynamic data in cross-chain interaction. EDCA can produce relatively small auditor states while maintaining the storage proof. Moreover, EDCA is proven to satisfy strong security and privacy guarantees, tag unforgeability, and proof unforgeability. Experimental evaluations confirm that EDCA has high computational and communicational efficiency and can retain a small and relatively constant auditing overhead.