Consumer Data Without Compromise: Integrating Differential Privacy and GANs for Privacy-Preserving Digital Marketing

Abstract

In the rapidly evolving digital marketing landscape, the utilization of consumer data is essential for efficient targeting and personalization of marketing practices. However, the growing concerns regarding user privacy and stringent data protection regulations have created challenges in accessing and using consumer data for marketing purposes. This paper introduces a novel approach that leverages Differential Privacy and Conditional Tabular Generative Adversarial Networks (CTGAN) to address these privacy concerns while maintaining the efficacy of data-driven digital marketing strategies. Our approach amalgamates the strengths of Differential Privacy and CTGAN, applying differential privacy to the original dataset to ensure that extracted data cannot be tied back to individuals. We then train a CTGAN on an open marketing dataset to learn and generate synthetic data of close resemblance. Through extensive empirical analysis, we evaluate the fidelity, utility, and trade-offs of our approach, demonstrating its effectiveness in synthesizing non-Gaussian and multi-modal distributions, and its applicability in real-world classification problems. The research also highlights the complexity of hyperparameter tuning and the importance of a balanced approach in model training. Our findings contribute valuable insights to both the theoretical understanding of generative models and practical guidance for digital marketing practitioners