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参考文献

Evaluating the Quality of Synthetic Data Generated with Deep Learning: A Systematic Review, Mark Lones, Mark D. Lee, Ryan E. Price, Christopher J. O'Neill, 2022 SN Computer Science, Vol. 3 (Springer Science and Business Media LLC) DOI: 10.1007/s42979-022-01456-4 - 对深度学习生成合成数据的评估方法进行系统回顾，涵盖统计相似性、效用和隐私。
The Algorithmic Foundations of Differential Privacy, Cynthia Dwork and Aaron Roth, 2014 (Now Publishers) - 一本基础性著作，定义了差分隐私并探讨其理论基础，对隐私评估非常重要。
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium, Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, Sepp Hochreiter, 2017 Advances in Neural Information Processing Systems, Vol. 30 (Curran Associates) DOI: 10.55989/fbfd - 介绍了Fréchet Inception Distance (FID)，一个广泛用于评估生成图像质量和多样性的指标。