Six Levels of Privacy: A Framework for Financial Synthetic Data

arXiv:2403.14724v1 Announce Type: cross
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 …

abstract applications arxiv benefits business client cs.cr cs.lg data financial financial applications framework information modeling privacy proprietary q-fin.st risks six synthetic synthetic data testing type