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 ideally...
- 3.Samarati P, Sweeney L. Protecting privacy when disclosing information: k-anonymity and its enforcement through generalization and suppression. Technicalreport, SRI International; 1998.Google Scholar
- 4.Truta TM, Vinay B. Privacy protection: p-sensitivek-anonymity property. In: Proceedings of the 2nd International Workshop on Privacy Data Management; 2006. p. 94.Google Scholar