Vertaile menetelmiä

	Differentiaalinen yksityisyys	Synteettisen datan generointi tietosuojan hallintaan
Tieteenala	Yksityisyydensuoja	Yksityisyydensuoja
Menetelmäperhe	Machine learning	Machine learning
Syntyvuosi	2006	1993
Kehittäjä	Cynthia Dwork	Donald Rubin
Tyyppi	Privacy-preserving randomized mechanism	Privacy-preserving data synthesis
Alkuperäislähde	Dwork, C. (2006). Differential privacy. International Colloquium on Automata, Languages and Programming (ICALP), 1–12.	Rubin, D. B. (1993). Statistical disclosure limitation. Journal of Official Statistics, 9(2), 461–468.
Rinnakkaisnimet	DP, epsilon-differential privacy, randomized privacy, Diferansiyel Gizlilik	Fully Synthetic Data, Partial Synthetic Data, Statistical Data Synthesis, Sentetik Veri Üretimi
Liittyvät	3	3
Tiivistelmä	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.	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.