Research on Information Security and Privacy Protection Model for Big Data

In the era of data flood, information security and privacy protection issues have become increasingly prominent, especially in the field of data integration and sharing and cross-border applications. Traditional encryption and decryption technologies have been overwhelmed and faced with severe challenges of huge data volumes and complex scenarios. This paper innovatively conceives an information security and privacy protection framework that integrates federated learning algorithms. Its core purpose is to accurately resolve the dual dilemma of information leakage and privacy erosion under the background of big data. Relying on the sophisticated mechanism of federated learning, this architecture builds a solid line of defense for user privacy in multi-party data collaboration scenarios, and simultaneously improves data computing performance and security. The system construction covers distributed data management components, privacy protection solutions, and deep learning-driven security evaluation algorithms. The three complement each other and jointly build a comprehensive security protection network. By using a series of actual big data sets to implement simulation experiments, the verification results show that this model significantly improves data processing accuracy under the premise of privacy protection, and shows excellent superiority in dimensions such as information security, privacy protection, and computing performance. Simulation data analysis further confirmed that the model surpasses traditional algorithms in key measurement indicators such as privacy exposure risk, computing cost, and model accuracy, demonstrating its broad application potential.