AI-driven Anonymization Techniques for Personalized Services in Online Retail: Balancing Privacy and Personalization

This article explores AI-driven anonymization techniques that enable online retailers to provide personalized services while protecting customer privacy. The investigation begins by examining the "personalization-privacy paradox," where consumers simultaneously desire customized experiences yet express concerns about data collection practices. A comprehensive literature review traces the evolution of privacy-preserving techniques in e-commerce and evaluates current anonymization methods, regulatory frameworks, and research gaps. The article then details four key anonymization methodologies: data masking, pseudonymization, differential privacy, and federated learning, highlighting their applications in retail contexts. An implementation framework follows, addressing privacy-first AI development, data governance structures, technical infrastructure requirements, and success metrics. Case studies demonstrate practical applications in personalized shopping experiences, customer behavior analysis, and real-time decision-making systems. Comparative analyses reveal how different approaches perform across various retail environments and product categories. The conclusion emphasizes that effective implementation requires balancing technical solutions with organizational governance while adapting to evolving privacy threats and consumer expectations.