Big data privacy protection and data security based on deep learning

Abstract

Computing systems managing large-scale, heterogeneous data are increasingly vulnerable to privacy breaches and adversarial attacks. This study presents a federated deep learning framework that systematically integrates adaptive privacy noise mechanisms and trust-weighted aggregation within a distributed architecture. The proposed method ensures the protection of sensitive data during collaborative analysis, even in highly dynamic environments, through precise differential privacy control and advanced neural network models. It is capable of real-time adjustments to privacy budgets and aggregation strategies to reduce the likelihood of information leakage while also lowering the risk for each node. Through comprehensive experiments integrating IoT sensor streams and medical transaction data, it has been demonstrated that the system consistently achieves high prediction accuracy while significantly reducing the success rate of participant inference and reconstruction attacks. Quantitative analysis confirms the strong robustness and cross-domain deployment scalability of this solution. Direct comparisons with state-of-the-art technologies show that privacy enhances resilience and significantly reduces utility loss. Providing feasible technical guidelines for secure and compliant big data analysis, it has verified the effectiveness of integrated privacy-preserving deep learning in protecting critical information infrastructure.