We investigate the relation between $t$-closeness, a well-known model of data anonymization, and $\alpha$-protection, a model of data discrimination. We show that $t$-closeness implies $bd(t)$-pro\-tec\-tion, for a bound function $bd()$ depending on the discrimination measure at hand. This allows us to adapt an inference control method, the \emph{Mondrian} multidimensional generalization technique, to the purpose of non-discrimination data protection. The parallel between the two analytical models raises intriguing issues on the interplay between data anonymization and non-discrimination research in data mining.
Data Anonymity Meets Non-Discrimination
RUGGIERI, SALVATORE
2013-01-01
Abstract
We investigate the relation between $t$-closeness, a well-known model of data anonymization, and $\alpha$-protection, a model of data discrimination. We show that $t$-closeness implies $bd(t)$-pro\-tec\-tion, for a bound function $bd()$ depending on the discrimination measure at hand. This allows us to adapt an inference control method, the \emph{Mondrian} multidimensional generalization technique, to the purpose of non-discrimination data protection. The parallel between the two analytical models raises intriguing issues on the interplay between data anonymization and non-discrimination research in data mining.File in questo prodotto:
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