Privacy-Enhanced AI Models for Personalized Recommendations

Abstract

The proliferation of artificial intelligence (AI) in personalized recommendation systems has significantly enhanced user experiences across various domains, including e-commerce, social media, and entertainment. However, this advancement comes with critical privacy concerns, as the extensive data collection required for personalization often intrudes on users' privacy. This paper explores the development and implementation of privacy-enhanced AI models for personalized recommendations, focusing on techniques such as differential privacy, federated learning, and homomorphic encryption. These methods aim to balance the trade-off between data utility and privacy preservation. Differential privacy ensures that individual data contributions are obscured within the dataset, providing robust privacy guarantees. Federated learning enables the training of AI models across decentralized devices without transmitting raw data, thereby minimizing privacy risks. Homomorphic encryption allows computations on encrypted data, ensuring that sensitive information remains protected throughout the processing pipeline. By integrating these privacy-preserving techniques, we can develop AI models that deliver accurate and personalized recommendations while safeguarding user privacy. This approach not only addresses regulatory and ethical concerns but also fosters user trust and acceptance of AI-driven personalization. Our findings demonstrate that privacy-enhanced AI models can achieve performance comparable to traditional methods, making them a viable solution for privacy-conscious applications in the era of big data.