Decoupling Identity from Utility: Privacy-by-Design Frameworks for Financial Ecosystems
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 correl...
| Authors | Ifayoyinsola Ibikunle, Tyler Farnan, Senthil Kumar et al. |
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| Primary category | cs.CE |
| Published | 16 April 2026 |
| Last updated at source | 16 April 2026 |
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