Comparer des méthodes

	Génération de données synthétiques pour le contrôle de la divulgation	Réseau antagoniste génératif
Domaine	Protection de la vie privée	Apprentissage profond
Famille	Machine learning	Machine learning
Année d'origine	1993	2014
Auteur d'origine	Donald Rubin	Goodfellow, I. et al.
Type	Privacy-preserving data synthesis	Generative deep learning (adversarial two-network game)
Source fondatrice	Rubin, D. B. (1993). Statistical disclosure limitation. Journal of Official Statistics, 9(2), 461–468.	Goodfellow, I. et al. (2014). Generative Adversarial Nets. NeurIPS.
Alias	Fully Synthetic Data, Partial Synthetic Data, Statistical Data Synthesis, Sentetik Veri Üretimi	Üretici Çekişmeli Ağ (GAN), GAN, generative adversarial nets, adversarial network
Apparentées	3	4
Résumé	Synthetic data generation is a statistical disclosure limitation technique introduced by Donald Rubin in 1993, in which values in a confidential dataset are replaced by draws from a fitted posterior predictive distribution rather than released directly. The resulting artificial records preserve the joint statistical structure of the original data while preventing the identification of real individuals, enabling analysts to work with a publicly releasable dataset that behaves like the original for most inferential purposes.	A Generative Adversarial Network (GAN), introduced by Ian Goodfellow and colleagues in 2014, produces realistic synthetic data through the competition of two neural networks — a generator and a discriminator. It is widely used for image synthesis, data augmentation, and distribution estimation.