Сравнение методов

	Оценка риска раскрытия (Disclosure Risk Assessment)	k-Анонимность: Защита индивидуальной конфиденциальности в публикуемых данных
Область	Конфиденциальность	Конфиденциальность
Семейство	Regression model	Machine learning
Год появления	1989	2002
Автор метода	George Duncan & Diane Lambert	Latanya Sweeney
Тип	Probabilistic risk model	Privacy-preserving data transformation
Основополагающий источник	Duncan, G. T., & Lambert, D. (1989). The risk of disclosure for microdata. Journal of Business & Economic Statistics, 7(2), 207–217.	Sweeney, L. (2002). k-anonymity: A model for protecting privacy. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 10(5), 557–570.
Другие названия	Microdata Disclosure Risk, Statistical Disclosure Control Risk Estimation, Istatistiksel Açıklama Riski Değerlendirmesi, Re-identification Risk Assessment	k-Anonymization, k-Anonymous Microdata, Quasi-Identifier Suppression Model, k-Anonimlik
Связанные	3	2
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.	k-Anonymity is a formal privacy model introduced by Latanya Sweeney in 2002 to protect individuals when personal data is released for research or public use. It requires that every record in a published dataset be indistinguishable from at least k−1 other records with respect to a designated set of quasi-identifying attributes — such as age, gender, and ZIP code — preventing re-identification by linking released data to external sources.