Evaluation of Differential Privacy and Federated Learning for AI-Driven Customer Service Applications

The proliferation of artificial intelligence in customer service applications has heightened concerns about the protection of sensitive consumer data. This paper presents a comprehensive comparative evaluation of privacy-preserving techniques, specifically differential privacy and federated learning, within AI-driven customer service contexts. A multidimensional evaluation framework is proposed to assess these techniques across security robustness, model accuracy, computational efficiency, and algorithmic fairness. An experimental analysis of customer interaction datasets reveals that federated learning achieves 94.2% accuracy retention while maintaining privacy guarantees, whereas differential privacy mechanisms offer superior protection against membership inference attacks at a 12.3% accuracy trade-off. The findings provide actionable recommendations for enterprises seeking to balance data protection compliance with service quality optimization, contributing to the development of trustworthy AI systems aligned with regulatory requirements.