Decoupling Identity from Utility: Privacy-by-Design Frameworks for Financial Ecosystems
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Abstract (arXiv)
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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References · 11
- 2023FinMem: A Performance-Enhanced LLM Trading Agent with Layered Memory and Character Designcited by 163
- 2020Synthetic Data -- Anonymisation Groundhog Daycited by 105
- 2021Winning the NIST Contest: A scalable and general approach to differentially private synthetic datacited by 101
- 2022AIM: An Adaptive and Iterative Mechanism for Differentially Private Synthetic Datacited by 88
- 2020FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Financecited by 86
- 2021Robin Hood and Matthew Effects: Differential Privacy Has Disparate Impact on Synthetic Datacited by 48
- 2021ABIDES-Gym: Gym Environments for Multi-Agent Discrete Event Simulation and Application to Financial Marketscited by 34
- 2019The Value of Collaboration in Convex Machine Learning with Differential Privacycited by 24
- 2023Privacy-Enhancing Technologies for Financial Data Sharingcited by 2
- 2021Financial Vision Based Differential Privacy Applicationscited by 1
- 2024Differentially Private Deep Model-Based Reinforcement Learningcited by 1
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