Paper: arXiv 2403.14724

Abstract

Synthetic Data is increasingly important in financial applications. In addition to the benefits it provides, such as improved financial modeling and better testing procedures, it poses privacy risks as well. Such data may arise from client information, business information, or other proprietary sources that must be protected. Even though the process by which Synthetic Data is generated serves to obscure the original data to some degree, the extent to which privacy is preserved is hard to assess. Accordingly, we introduce a hierarchy of levels’’ of privacy that are useful for categorizing Synthetic Data generation methods and the progressively improved protections they offer. While the six levels were devised in the context of financial applications, they may also be appropriate for other industries as well. Our paper includes: A brief overview of Financial Synthetic Data, how it can be used, how its value can be assessed, privacy risks, and privacy attacks. We close with details of the Six Levels’’ that include defenses against those attacks.

Complexity vs Empirical Score

  • Math Complexity: 4.0/10
  • Empirical Rigor: 1.5/10
  • Quadrant: Philosophers — conceptual discussion, limited math and data

Why this score: The paper proposes a conceptual framework with low mathematical density, focusing on definitions and categorization rather than complex modeling or proofs. Empirical rigor is minimal, as it lacks backtests, code, or implementation details, instead relying on theoretical discussions of privacy risks and regulatory compliance.

Research Flowchart

  flowchart TD
  A["Research Goal<br>Define & Categorize<br>Privacy Levels in Financial<br>Synthetic Data"] --> B["Methodology<br>Literature Review & Attack Analysis"]
  B --> C["Data/Input<br>Financial Synthetic Data<br>Privacy Attacks"]
  C --> D["Computational Process<br>Apply Threat Models &<br>Privacy Preservation Techniques"]
  D --> E["Key Finding 1<br>Identified 6 Levels of Privacy<br>Hierarchy for Protection"]
  D --> F["Key Finding 2<br>Framework for Assessing<br>Privacy Risks in Finance"]

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  366. Implicit generation and modeling with energy based models (2019)
  367. Multilayer feedforward networks are universal approximators (1989)
  368. Tabddpm: Modelling tabular data with diffusion models (2023)
  369. Model explanations with differential privacy (2022)
  370. Deepfakes: Trick or treat? (2020)
  371. Digital signatures (2010)

2 of 379 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.