In the process of collecting, storing, transmitting, and analyzing big data, data privacy is easily threatened by leakage. There is a risk of tampering or illegal access to traditional data storage methods. To this end, this article first collects network security data through the Particle Swarm Optimization (PSO) algorithm, and improves the local optimization efficiency of the algorithm by introducing inertia weight factors that decrease in a horizontal parabolic pattern (opening to the left) to optimize the optimal state of network security. Then, the article uses data collection tools for real-time data processing and analysis to improve data availability. Subsequently, user access verification and data protection are carried out through blockchain technology, encrypting data to ensure its integrity. Finally, the experiment is simulated using Python 3.8 and a data analysis library. The experimental results show that the accuracy of PSO algorithm is between 0.85 and 0.95 in 20 iterations. The increase in privacy budget significantly reduces the success rate of attacks and the number of security incidents, while accelerating recovery time. Overall, the increase in privacy budget significantly enhances the system’s data protection and defense capabilities.