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

• URL: http://arxiv.org/abs/2604.14495v1
• Date: Thu, 16 Apr 2026 00:07:32 GMT
• Title: Decoupling Identity from Utility: Privacy-by-Design Frameworks for Financial Ecosystems
• Abstract summary: This paper explores Differentially Private (DP) synthetic data as a robust "Privacy by Design" framework to resolve this conflict.<n>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.
• Score: 2.8555760710139393
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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