Six Levels of Privacy: A Framework for Financial Synthetic Data

arXiv:2403.14724v1 Announce Type: new
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 …

addition applications arxiv benefits business client cs.cr cs.lg data financial framework important information may modeling privacy privacy risks procedures risks six synthetic synthetic data testing