In today’s data-driven business landscape, the imperative to merge meticulous customer segmentation with stringent privacy standards has never been more pressing. This research introduces a pioneering approach that achieves this synthesis, employing differential privacy and k-anonymity in tandem with advanced clustering techniques. The study rigorously protects personal data while delineating customer purchasing behaviors from a credit card transaction dataset over a six-month period. The novel application of DBSCAN, enhanced by privacy-preserving methods, allows for the extraction of distinct customer segments, yielding actionable insights for tailored marketing strategies. The research demonstrates that the implementation of k-anonymity not only complements but also augments the analytical prowess of K-means clustering. This confluence of privacy and analytics extends the boundaries of current methodologies, providing a blueprint for future investigations into ethical, privacy-conscious data utilization in segmentation and beyond. Our findings assert the feasibility of upholding individual privacy without forgoing the granularity required for strategic business decision-making, marking a significant stride in the conscientious use of big data.