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arXiv · 2604.14495

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

Ifayoyinsola Ibikunle·Tyler Farnan·Senthil Kumar·Mayana Pereira
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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.

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Ifayoyinsola Ibikunle, Tyler Farnan, Senthil Kumar, Mayana Pereira. 2026-04-16. Decoupling Identity from Utility: Privacy-by-Design Frameworks for Financial Ecosystems. https://arxiv.org/abs/2604.14495

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