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This paper explores the strategic use of modern synthetic data generation andadvanced data perturbation techniques to enhance security, maintain analyticalutility, and improve operational efficiency when managing large datasets, witha particular focus on the Banking, Financial Services, and Insurance (BFSI)sector. We contrast these advanced methods encompassing generative models likeGANs, sophisticated context-aware PII transformation, configurable statisticalperturbation, and differential privacy with traditional anonymizationapproaches.  The goal is to create realistic, privacy-preserving datasets that retain highutility for complex machine learning tasks and analytics, a critical need inthe data-sensitive industries like BFSI, Healthcare, Retail, andTelecommunications. We discuss how these modern techniques potentially offersignificant improvements in balancing privacy preservation while maintainingdata utility compared to older methods. Furthermore, we examine the potentialfor operational gains, such as reduced overhead and accelerated analytics, byusing these privacy-enhanced datasets. We also explore key use cases wherethese methods can mitigate regulatory risks and enable scalable, data-driveninnovation without compromising sensitive customer information.

Optimizing the Privacy-Utility Balance using Synthetic Data and Configurable Perturbation Pipelines