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

arXiv Security Archived 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 Focus to learn more Submission history From: Ifayoyinsola Ibikunle [view email] [v1] Thu, 16 Apr 2026 00:07:32 UTC (25 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CE < prev   |   next > new | recent | 2026-04 Change to browse by: cs cs.AI cs.CR References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)
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    arXiv Security
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    ◬ AI & Machine Learning
    Published
    Apr 17, 2026
    Archived
    Apr 17, 2026
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