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

TL;DR

This paper tackles the privacy-utility trade-off in BFSI data by contrasting traditional anonymization with modern privacy-preserving approaches that combine purely synthetic data generation and configurable perturbation pipelines. It surveys foundational mechanisms (DP, geometric, exponential, Laplace, Gaussian, randomized response) and introduces advanced steps like context-aware PII transformation and DP-enabled generative modeling (e.g., DP-SGD). The authors argue that a configurable mix of synthetic data, per-attribute perturbation, and PII-aware transformations can achieve higher utility while meeting regulatory privacy requirements, with concrete BFSI use cases for fraud detection, risk assessment, and data sharing. The work highlights practical benefits—improved analytics, operational efficiency, and scalable data-driven innovation—while acknowledging challenges in noise calibration, binning bias, and maintaining data fidelity. Overall, the paper provides a framework for balancing privacy and utility in privacy-preserving data pipelines and outlines directions for integrating these techniques into real-world BFSI analytics and compliance workflows.

Abstract

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.

Figures (8)

• Figure 1: Clipping: Original Data vs. Clipped Data
• Figure 2: Highlevel view of advanced Differential Privacy
• Figure 3: Randomized Response Implementation
• Figure 4: Laplace Mechanism Process
• Figure 5: Gaussian Mechanism Process