The optimization of batch processing and micro-payment systems in account-based anonymous blockchain systems

Abstract

Account-based anonymous blockchain systems can provide robust privacy protection for users. However, they become highly inefficient when handling high-frequency micro-payment scenarios. This paper presents systematic optimizations for batch processing and micro-payment transactions in account-based anonymous blockchain systems to enhance both privacy and efficiency. Building on BlockMaze, the first account-based anonymous blockchain system fully protecting transaction privacy, we propose innovations in batch transfers, batch receipts, and micro-payment handling. By reducing redundant data, improving circuit design, and optimizing zk-SNARK proof generation, we achieve up to 55.90% and 23.02% reductions in overall time consumption for batch transfers and receipts, respectively, significantly cutting computational cost and memory use. For micro-payments, a solution encapsulating the payment deadline reduces transaction delays and fund freezing. Experimental results show only slight increases in proof generation time—1.41 seconds for transfers and 1.02 seconds for payments—while maintaining privacy protection. This research lays a foundation for practical applications of account-based anonymous blockchain systems, enhancing privacy, processing efficiency, and transferability to other systems. • Optimized batch processing and improve transaction efficiency in account-based anonymous blockchain systems. • Optimized circuit design reduces redundant data and shortens zero-knowledge proof times. • Time consumption decreased by up to 55.90% in batch transfer function and 23.02% in batch receipt function. • Highly transferable to other account-based anonymous blockchain systems, offering strong flexibility and application potential. • Offers future research directions to improve blockchain efficiency and privacy protection.