Synthetic data generation, a cornerstone of Generative Artificial Intelligence, promotes a paradigm shift in data science by addressing data scarcity and privacy while enabling unprecedented performance. As synthetic data becomes more prevalent, concerns emerge regarding the accuracy of statistical methods when applied to synthetic data in contrast to raw data. This article explores the effectiveness of statistical methods on synthetic data and the privacy risks of synthetic data. Regarding effectiveness, we present the Synthetic Data Generation for Analytics framework. This framework applies statistical approaches to high-quality synthetic data produced by generative models like tabular diffusion models, which, initially trained on raw data, benefit from insights from pertinent studies through transfer learning. A key finding within this framework is the generational effect, which reveals that the error rate of statistical methods on synthetic data decreases with the addition of more synthetic data but may eventually rise or stabilize. This phenomenon, stemming from the challenge of accurately mirroring raw data distributions, highlights a "reflection point"-an ideal volume of synthetic data defined by specific error metrics. Through three case studies, sentiment analysis, predictive modeling of structured data, and inference in tabular data, we validate the superior performance of this framework compared to conventional approaches. On privacy, synthetic data imposes lower risks while supporting the differential privacy standard. These studies underscore synthetic data's untapped potential in redefining data science's landscape.

翻译：合成数据生成作为生成式人工智能的基石，通过解决数据稀缺性与隐私保护问题，同时实现前所未有的性能表现，推动了数据科学的范式变革。随着合成数据的日益普及，人们开始关注相较于原始数据，统计方法应用于合成数据时的准确性。本文探讨了统计方法在合成数据上的有效性及其隐私风险。在有效性方面，我们提出了“面向分析框架的合成数据生成”体系，该框架将统计方法应用于由表格扩散模型等生成模型产生的高质量合成数据——这些模型最初基于原始数据训练，并通过迁移学习吸收相关研究的洞见。该框架的关键发现是“世代效应”：合成数据上的统计方法错误率随数据量增加而下降，但最终可能回升或趋于稳定。这一现象源于准确镜像原始数据分布的挑战，揭示了由特定误差指标定义的合成数据“反射点”——即理想数据量。通过情感分析、结构化数据预测建模及表格数据推理三个案例研究，我们验证了该框架相较于传统方法的卓越性能。在隐私方面，合成数据在支持差分隐私标准的同时降低了隐私风险。这些研究凸显了合成数据在重新定义数据科学格局方面的未竟潜力。