Machine learningPrivacy-preserving analysis

k-Anonymity: Protecting Individual Privacy in Released Data

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.

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Sources

  1. Sweeney, L. (2002). k-anonymity: A model for protecting privacy. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 10(5), 557–570. DOI: 10.1142/S0218488502001648

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Referenced by

ScholarGatek-Anonymity (k-Anonymity Data Anonymization). Retrieved 2026-06-04 from https://scholargate.app/en/privacy/k-anonymity