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Protecting individual privacy is an important problem in microdata distribution and publishing. Anonymization algorithms typically aim to satisfy certain privacy definitions with minimal impact on the quality of the resulting data. While much of the previous literature has measured quality through simple one-size-fits-all measures, the authors argue that quality is best judged with respect to the workload for which the data will ultimately be used. This paper provides a suite of anonymization algorithms that incorporate a target class of workloads, consisting of one or more data mining tasks as well as selection predicates.
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