Blockchain Privacy Transaction Optimization Model Based on Zero-Knowledge Proof

Abstract

With the widespread application of blockchain technology, the security of private transactions has become a bottleneck restricting further development. This project presents a blockchain privacy transaction optimization model utilizing zero-knowledge proof (ZKP). By extracting data features such as transaction volume, transaction frequency, and counterparty trustworthiness, the model dynamically assigns weights through an entropy-based framework for different transaction scenarios. It also adaptively modifies certificate generation and verification strategies using reinforcement learning to enhance efficiency and security. In terms of experiments, a blockchain simulation environment is constructed, and 100,000 transaction data points are used as samples to compare the DA-ZKP algorithm and the traditional zero-knowledge proof algorithm. The experimental results show that the DA-ZKP algorithm reduces the generation time by 35%, the verification time by 28%, and the memory overhead by 22% on average. At the same time, the algorithm has a privacy protection capability comparable to traditional algorithms and can resist replay and tampering attacks. The optimization model and algorithm proposed in this project can effectively improve the efficiency and security of blockchain privacy transactions and provide a new idea for developing blockchain privacy protection technology.