With the rapid development of big data technology, big data has become an important foundation for research and application in various fields. However, the rapid growth of big data not only brings unprecedented data processing challenges but also significantly increases the risk of personal privacy leakage. This article focuses on the research of intelligent electronic information security and privacy protection in big data analysis, aiming to provide a comprehensive overview of big data privacy protection mechanisms and deeply analyze the challenges faced by existing mechanisms. Through comparative analysis, this study finds problems and shortcomings in the efficiency, accuracy, and practicality of existing privacy protection mechanisms. In response to these issues, this study has designed a more efficient and comprehensive privacy protection framework as well as privacy protection strategies that are more suitable for different application scenarios. The final research results indicate that the network security authentication time based on the method proposed in this paper is the shortest, with a processing time of only 8.45 s under a 3000 sample size. Moreover, the information coverage rate of the method proposed in this paper is the highest, reaching 98%.