A framework for dynamic blockchain-based data auditing

Abstract

Accounting transactions and business data stored in blockchains are secure and resistant to tampering. Nonetheless, maintaining consistency between actual on– and off-chain data in blockchain auditing continues to be a significant challenge. We propose a dynamic validation model for blockchain data to address this challenge. Specifically, we adjusted the parameter selection rules in the traditional Rivest-Shamir-Adleman algorithm to assess the consistency between on-chain and off-chain factual data. We also incorporated random numbers into the Merkle Hash Tree algorithm to verify the equivalency of on- and off-chain actual data while maintaining quantity consistency. The model improves the integrity and authenticity of the data used in auditing and increases the credibility of the audit environment. Our study contributes significantly to the limited literature on using blockchain technology in auditing.