衡量金融合成数据生成中的隐私风险与权衡
Measuring Privacy Risks and Tradeoffs in Financial Synthetic Data Generation
AI总结：
本文研究了金融领域合成数据生成中隐私与效用的权衡，通过改进GAN和自编码器的隐私保护实现，评估了不同数据集平衡性对生成质量的影响。
摘要：
我们探讨了在表格金融数据集上合成数据生成方案的隐私-效用权衡，这是一个以高监管风险和严重类别不平衡为特征的领域。我们考虑了代表性的表格数据生成器，包括自编码器、生成对抗网络、扩散模型和皮尔逊合成器。为了解决金融领域的挑战，我们提供了GAN和自编码器生成器的新型隐私保护实现。我们评估生成器是否以及如何同时实现数据质量、下游效用和隐私，通过平衡和不平衡输入数据集的比较。我们的结果提供了对从表现出严重类别不平衡和混合类型属性的数据集生成合成数据的挑战的深入见解。
英文摘要：
We explore the privacy-utility tradeoff of synthetic data generation schemes on tabular financial datasets, a domain characterized by high regulatory risk and severe class imbalance. We consider representative tabular data generators, including autoencoders, generative adversarial networks, diffusion, and copula synthesizers. To address the challenges of the financial domain, we provide novel privacy-preserving implementations of GAN and autoencoder synthesizers. We evaluate whether and how well the generators simultaneously achieve data quality, downstream utility, and privacy, with comparison across balanced and imbalanced input datasets. Our results offer insight into the distinct challenges of generating synthetic data from datasets that exhibit severe class imbalance and mixed-type attributes.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
