Decoupling Identity from Utility: Privacy-by-Design Frameworks for Financial Ecosystems
arXiv SecurityArchived Apr 17, 2026✓ Full text saved
arXiv:2604.14495v1 Announce Type: cross 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 reconstr
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Computer Science > Computational Engineering, Finance, and Science
[Submitted on 16 Apr 2026]
Decoupling Identity from Utility: Privacy-by-Design Frameworks for Financial Ecosystems
Ifayoyinsola Ibikunle, Tyler Farnan, Senthil Kumar, Mayana Pereira
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.
Subjects: Computational Engineering, Finance, and Science (cs.CE); Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
Cite as: arXiv:2604.14495 [cs.CE]
(or arXiv:2604.14495v1 [cs.CE] for this version)
https://doi.org/10.48550/arXiv.2604.14495
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From: Ifayoyinsola Ibikunle [view email]
[v1] Thu, 16 Apr 2026 00:07:32 UTC (25 KB)
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