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.

合成数据在金融应用中日益重要，除了提供改进的金融建模和更好的测试程序之外，它也带来了隐私风险。本文引入了一个 “级别” 隐私层次结构，用于分类合成数据生成方法和它们提供的逐步改进的保护措施，以评估隐私保护的程度。同时，我们介绍了金融合成数据的概述、使用方式、价值评估、隐私风险和隐私攻击，并详细介绍了 “六个级别” 的防御措施。