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On the Approximability of Geometric and Geographic Generalization and the Min-Max Bin Covering Problem
[chapter]
2009
Lecture Notes in Computer Science
We study the problem of abstracting a table of data about individuals so that no selection query can identify fewer than k individuals. As is common in existing work on this k-anonymization problem, the means we investigate to perform this anonymization is to generalize values of quasi-identifying attributes into equivalence classes. Since such data tables are intended for use in data mining, we consider the natural optimization criterion of minimizing the maximum size of any equivalence class,
doi:10.1007/978-3-642-03367-4_22
fatcat:lgrw2nrhqfcdzn7mhsb6nnfq4y