Decoupling Identity from Utility: Privacy-by-Design Frameworks for Financial Ecosystems
Ifayoyinsola Ibikunle, Tyler Farnan, Senthil Kumar, Mayana Pereira
TLDR
Decoupling identity from utility, this paper presents DP synthetic data frameworks for financial ecosystems, balancing data utility and privacy.
Key contributions
- Proposes Differentially Private (DP) synthetic data as a "Privacy by Design" framework for financial ecosystems.
- Explores Direct Tabular Synthesis for high-fidelity joint distributions and static historical correlations.
- Introduces DP-Seeded Agent-Based Modeling for dynamic market behaviors and "counterfactual" simulations.
- Decouples identity from utility, enabling seamless cross-institutional research and compliant decision-making.
Why it matters
This paper offers a robust solution to a critical challenge in finance: balancing data utility with privacy. It introduces DP synthetic data frameworks, enabling institutions to conduct advanced analytics and simulations while adhering to strict regulations. This fosters innovation and collaboration without compromising individual privacy.
Original 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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