Research on the Application of Artificial Intelligence in Consumer Privacy Data Protection

This paper systematically explores the application of artificial intelligence in protecting consumer privacy data, establishing a comprehensive technical framework encompassing data collection and preprocessing, model development, inference and result presentation, and continuous operational tracking. This experiment uses the 2024 consumer complaint data from the U.S. Consumer Financial Protection Bureau as the research object, implementing multiple privacy protection methods such as differential privacy, k-anonymization, federated learning, homomorphic encryption, and interpretability risk assessment. A quantitative assessment of leakage rate and compliance was conducted. After evaluating leakage rate and compliance, it was found that reducing the privacy budget can effectively reduce data leakage and ensure high-standard compliance with the CCPA and GDPR. Experimental results show that within the framework of combining homomorphic encryption and federated learning, the average model loss continues to decrease, stable convergence is achieved within a limited number of training rounds, and overall computational efficiency is maintained at an acceptable level. This framework balances privacy protection and regulatory enforcement, achieving high-performance computing, and providing a practical technical path and empirical support for protecting U.S. consumer privacy.