Сравнение методов

	Генерация синтетических данных для контроля раскрытия информации	Дифференциальная приватность
Область	Конфиденциальность	Конфиденциальность
Семейство	Machine learning	Machine learning
Год появления	1993	2006
Автор метода	Donald Rubin	Cynthia Dwork
Тип	Privacy-preserving data synthesis	Privacy-preserving randomized mechanism
Основополагающий источник	Rubin, D. B. (1993). Statistical disclosure limitation. Journal of Official Statistics, 9(2), 461–468.	Dwork, C. (2006). Differential privacy. International Colloquium on Automata, Languages and Programming (ICALP), 1–12.
Другие названия	Fully Synthetic Data, Partial Synthetic Data, Statistical Data Synthesis, Sentetik Veri Üretimi	DP, epsilon-differential privacy, randomized privacy, Diferansiyel Gizlilik
Связанные	3	3
Сводка	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.	Differential privacy is a mathematical framework for releasing statistical information about a dataset while providing rigorous guarantees that individual records cannot be identified or inferred. Introduced by Cynthia Dwork in 2006, it formalizes privacy as a probabilistic bound: any single individual's presence or absence in the dataset changes the output distribution by at most a multiplicative factor of e^ε, where ε is the privacy budget controlling the privacy–utility tradeoff.