Decoupling Identity from Utility: Privacy-by-Design Frameworks for Financial Ecosystems

TL;DR

This paper proposes Differential Privacy-based synthetic data frameworks for financial ecosystems to balance data utility with privacy, enabling secure, compliant cross-institutional research.

Contribution

It introduces two novel DP synthetic data paradigms—Direct Tabular Synthesis and DP-Seeded ABM—for static and dynamic financial data modeling.

Findings

Tabular synthesis effectively captures static historical correlations.

DP-Seeded ABM models dynamic market behaviors and black swan events.

Both methods facilitate privacy-preserving, cross-institutional financial research.

Abstract

Financial institutions face tension between maximizing data utility and mitigating the re-identification risks inherent in traditional anonymization methods. This paper explores Differentially Private (DP) synthetic data as a robust "Privacy by Design" framework to resolve this conflict, ensuring output privacy while satisfying stringent regulatory obligations. We examine two distinct generative paradigms: Direct Tabular Synthesis, which reconstructs high-fidelity joint distributions from raw data, and DP-Seeded Agent-Based Modeling (ABM), which uses DP-protected aggregates to parameterize complex, stateful simulations. While tabular synthesis excels at reflecting static historical correlations for QA testing and business analytics, the DP-Seeded ABM offers a forward-looking "counterfactual laboratory" capable of modeling dynamic market behaviors and black swan events. By decoupling individual identities from data utility, these methodologies eliminate traditional data-clearing bottlenecks, enabling seamless cross-institutional research and compliant decision-making in an evolving regulatory landscape.