Six Levels of Privacy: A Framework for Financial Synthetic Data
Tucker Balch Vamsi K. Potluru Deepak Paramanand Manuela Veloso
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.