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| Prywatność różnicowa× | k-Anonimowość: Ochrona prywatności jednostki w udostępnianych danych× | |
|---|---|---|
| Dziedzina | Prywatność | Prywatność |
| Rodzina | Machine learning | Machine learning |
| Rok powstania≠ | 2006 | 2002 |
| Twórca≠ | Cynthia Dwork | Latanya Sweeney |
| Typ≠ | Privacy-preserving randomized mechanism | Privacy-preserving data transformation |
| Źródło pierwotne≠ | Dwork, C. (2006). Differential privacy. International Colloquium on Automata, Languages and Programming (ICALP), 1–12. DOI ↗ | Sweeney, L. (2002). k-anonymity: A model for protecting privacy. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 10(5), 557–570. DOI ↗ |
| Inne nazwy | DP, epsilon-differential privacy, randomized privacy, Diferansiyel Gizlilik | k-Anonymization, k-Anonymous Microdata, Quasi-Identifier Suppression Model, k-Anonimlik |
| Pokrewne≠ | 3 | 2 |
| Podsumowanie≠ | 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. |
| ScholarGateZbiór danych ↗ |
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