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EnglishWe 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.
| Titolo: | Data anonymity meets non-discrimination |
| Autori interni: | |
| Anno del prodotto: | 2013 |
| Serie: | |
| 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. |
| Handle: | http://hdl.handle.net/11568/291555 |
| ISBN: | 9781479931446 |
| Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
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