@article {984,
	title = {Using t-closeness anonymity to control for non-discrimination.},
	journal = {Trans. Data Privacy},
	volume = {7},
	number = {2},
	year = {2014},
	pages = {99{\textendash}129},
	abstract = {We investigate the relation between t-closeness, a well-known model of data anonymization
against attribute disclosure, and α-protection, a model of the social discrimination hidden in
data. We show that t-closeness implies bdf (t)-protection, for a bound function bdf () depending on
the discrimination measure f() at hand. This allows us to adapt inference control methods, such
as the Mondrian multidimensional generalization technique and the Sabre bucketization and redistribution
framework, 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 protection.},
	url = {http://dl.acm.org/citation.cfm?id=2870623},
	author = {Salvatore Ruggieri}
}
