Сравнение методов

	Дифференциальная приватность	k-Анонимность: Защита индивидуальной конфиденциальности в публикуемых данных
Область	Конфиденциальность	Конфиденциальность
Семейство	Machine learning	Machine learning
Год появления	2006	2002
Автор метода	Cynthia Dwork	Latanya Sweeney
Тип	Privacy-preserving randomized mechanism	Privacy-preserving data transformation
Основополагающий источник	Dwork, C. (2006). Differential privacy. International Colloquium on Automata, Languages and Programming (ICALP), 1–12.	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	DP, epsilon-differential privacy, randomized privacy, Diferansiyel Gizlilik	k-Anonymization, k-Anonymous Microdata, Quasi-Identifier Suppression Model, k-Anonimlik
Связанные	3	2
Сводка	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.	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.