Decoupling Identity from Utility: Privacy-by-Design Frameworks for Financial Ecosystems
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866917412039819264 |
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| author | Ibikunle, Ifayoyinsola Farnan, Tyler Kumar, Senthil Pereira, Mayana |
| author_facet | Ibikunle, Ifayoyinsola Farnan, Tyler Kumar, Senthil Pereira, Mayana |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_14495 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Decoupling Identity from Utility: Privacy-by-Design Frameworks for Financial Ecosystems Ibikunle, Ifayoyinsola Farnan, Tyler Kumar, Senthil Pereira, Mayana Computational Engineering, Finance, and Science Artificial Intelligence Cryptography and Security 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. |
| title | Decoupling Identity from Utility: Privacy-by-Design Frameworks for Financial Ecosystems |
| topic | Computational Engineering, Finance, and Science Artificial Intelligence Cryptography and Security |
| url | https://arxiv.org/abs/2604.14495 |