Protecting data privacy in the context of big data has become a pressing issue in machine learning. Traditional data protection techniques often struggle to ensure user privacy while maintaining learning efficiency and model accuracy. Differential privacy offers a mathematically provable method for privacy protection, effectively reducing the influence of individual data points on model training and preventing data leakage and other security risks. However, in large-scale learning scenarios, differential privacy faces challenges such as high computational costs and performance degradation due to gradient noise. This project conducts an in-depth analysis of privacy protection requirements in big data environments and identifies the limitations of existing differential privacy techniques. Based on this analysis, the project investigates differential privacy training methods tailored for big data applications. Specifically, it integrates techniques such as gradient clipping optimization, dynamic privacy budget allocation, and efficient noise addition to enhance privacy during training while minimizing performance loss. Experimental results demonstrate that the proposed approach can effectively improve learning efficiency and prediction accuracy in large-scale models, while ensuring user privacy. The findings of this research provide a novel approach to privacy protection in big data environments and offer technical support for its practical implementation.