Salīdzināt metodes

	k-Anonimitāte: individuālās privātuma aizsardzība publicētajos datos	Sintētisko datu ģenerēšana atklāšanas kontrolei
Nozare	Privātums	Privātums
Saime	Machine learning	Machine learning
Izcelsmes gads	2002	1993
Autors	Latanya Sweeney	Donald Rubin
Tips	Privacy-preserving data transformation	Privacy-preserving data synthesis
Pirmavots	Sweeney, L. (2002). k-anonymity: A model for protecting privacy. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 10(5), 557–570.	Rubin, D. B. (1993). Statistical disclosure limitation. Journal of Official Statistics, 9(2), 461–468.
Citi nosaukumi	k-Anonymization, k-Anonymous Microdata, Quasi-Identifier Suppression Model, k-Anonimlik	Fully Synthetic Data, Partial Synthetic Data, Statistical Data Synthesis, Sentetik Veri Üretimi
Saistītās	2	3
Kopsavilkums	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.	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.