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If, for a given k, k-anonymity is assumed to be sufficient protection, one can concentrate on minimizing information loss with the only constraint that k-anonymity should be satisfied. This is a clean way of solving the tension between data protection and data utility. Since k-anonymity is usually achieved via generalization (equivalent to global recoding, as said above) and local suppression, minimizing information loss usually translates to reducing the number and/or the magnitude of suppressions.
k-Anonymity bears some resemblance to the underlying principle of microaggregation and is a useful concept because quasi-identifiers are usually categorical or can be categorized, i.e., they take values in a finite (and...
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Recommended Reading
Domingo-Ferrer J, Torra V. Ordinal, continuous and heterogenerous k-anonymity through microaggregation. Data Mining Knowl Discov. 2005;11(2):195–212.
Samarati P. Protecting respondents’ identities in microdata release. IEEE Trans Knowl Data Eng. 2001;13(6):1010–27.
Samarati P, Sweeney L. Protecting privacy when disclosing information: k-anonymity and its enforcement through generalization and suppression. Technicalreport, SRI International; 1998.
Truta TM, Vinay B. Privacy protection: p-sensitivek-anonymity property. In: Proceedings of 2nd International Workshop on Privacy Data Management; 2006. p. 94.
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Domingo-Ferrer, J. (2014). k-Anonymity. In: Liu, L., Özsu, M. (eds) Encyclopedia of Database Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4899-7993-3_1503-2
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DOI: https://doi.org/10.1007/978-1-4899-7993-3_1503-2
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