Comparer des méthodes

	k-Anonymity : Protection de la vie privée individuelle dans les données publiées	Confidentialité différentielle
Domaine	Protection de la vie privée	Protection de la vie privée
Famille	Machine learning	Machine learning
Année d'origine	2002	2006
Auteur d'origine	Latanya Sweeney	Cynthia Dwork
Type	Privacy-preserving data transformation	Privacy-preserving randomized mechanism
Source fondatrice	Sweeney, L. (2002). k-anonymity: A model for protecting privacy. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 10(5), 557–570.	Dwork, C. (2006). Differential privacy. International Colloquium on Automata, Languages and Programming (ICALP), 1–12.
Alias	k-Anonymization, k-Anonymous Microdata, Quasi-Identifier Suppression Model, k-Anonimlik	DP, epsilon-differential privacy, randomized privacy, Diferansiyel Gizlilik
Apparentées	2	3
Résumé	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.	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.