Optimizing the Privacy-Utility Balance using Synthetic Data and Configurable Perturbation Pipelines
It addresses privacy-utility challenges for data-sensitive industries like BFSI, Healthcare, Retail, and Telecommunications, but appears incremental as it contrasts existing methods without presenting new results.
This paper tackles the problem of balancing privacy and utility in large datasets, particularly for the BFSI sector, by exploring synthetic data generation and advanced perturbation techniques, which potentially offer improvements over traditional anonymization methods.
This paper explores the strategic use of modern synthetic data generation and advanced data perturbation techniques to enhance security, maintain analytical utility, and improve operational efficiency when managing large datasets, with a particular focus on the Banking, Financial Services, and Insurance (BFSI) sector. We contrast these advanced methods encompassing generative models like GANs, sophisticated context-aware PII transformation, configurable statistical perturbation, and differential privacy with traditional anonymization approaches. The goal is to create realistic, privacy-preserving datasets that retain high utility for complex machine learning tasks and analytics, a critical need in the data-sensitive industries like BFSI, Healthcare, Retail, and Telecommunications. We discuss how these modern techniques potentially offer significant improvements in balancing privacy preservation while maintaining data utility compared to older methods. Furthermore, we examine the potential for operational gains, such as reduced overhead and accelerated analytics, by using these privacy-enhanced datasets. We also explore key use cases where these methods can mitigate regulatory risks and enable scalable, data-driven innovation without compromising sensitive customer information.