Сравнение методов

	Оценка риска раскрытия (Disclosure Risk Assessment)	Дифференциальная приватность
Область	Конфиденциальность	Конфиденциальность
Семейство	Regression model	Machine learning
Год появления	1989	2006
Автор метода	George Duncan & Diane Lambert	Cynthia Dwork
Тип	Probabilistic risk model	Privacy-preserving randomized mechanism
Основополагающий источник	Duncan, G. T., & Lambert, D. (1989). The risk of disclosure for microdata. Journal of Business & Economic Statistics, 7(2), 207–217.	Dwork, C. (2006). Differential privacy. International Colloquium on Automata, Languages and Programming (ICALP), 1–12.
Другие названия	Microdata Disclosure Risk, Statistical Disclosure Control Risk Estimation, Istatistiksel Açıklama Riski Değerlendirmesi, Re-identification Risk Assessment	DP, epsilon-differential privacy, randomized privacy, Diferansiyel Gizlilik
Связанные	3	3
Сводка	Disclosure Risk Assessment is a probabilistic framework introduced by Duncan and Lambert (1989) for quantifying how likely it is that releasing microdata — individual-level records from surveys or administrative files — will allow an outside party to identify a specific respondent or infer sensitive attributes. It is used by statistical agencies, data custodians, and researchers charged with protecting confidentiality before any public release of person-level datasets.	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.