Six Levels of Privacy: A Framework for Financial Synthetic Data
This provides a framework for evaluating privacy risks in financial applications, but it is incremental as it builds on existing concepts without introducing new methods.
The paper tackles the challenge of assessing privacy preservation in financial synthetic data by introducing a six-level hierarchy to categorize generation methods and their protections, though no concrete performance numbers are provided.
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.