ABSTRACT
Background information: Aggregation of considerable customer information through e-commerce platforms improves the shopping experience, thus bringing forth the most important issues of data privacy. While personalization of recommendations is growing, so is the difficulty for the regulators to strike a balance between personalization and consumer protection. The paper addresses an integrated approach combining PGNN, DBSCAN-GA, and Federated Learning that maximizes online retail spaces while protecting privacy.
Methods: Federated learning is an approach that will decentralize the training of machine learning models on user devices, keeping private data private without hurting outcome quality. DBSCAN-GA groups customer actions by how they link up rather than by who they belong to, noticing tendencies without names attached. PGNN uses prediction on made-up information, finding formulas with no real people publicly displayed. In total, these methods make sure continuous improvement, accuracy, and following the rules that protect user details.
Objectives: The approach optimizes personal experience across digital platforms with subtle predictions that avoid profiling. It identifies repeated patterns of anonymized user behavior in order to increase relevance while being privacy-friendly through distributed learning and statutory obligations.
Results: The integrated model demonstrated great benefits such as scalability adaptability, high accuracy at 94.8%, and rigorous anonymization compared to traditional techniques. It can perform complex digital interactions efficiently and in a privacy-centric way.
Conclusions: The synergy of PGNN, DBSCAN-GA and Federated Learning constructs a balanced framework for customized connections without compromising user privacy or compliance with governing laws, paving promising avenues for innovative, privacy-preserving solutions in digital environments.439
