01114nas a2200121   4500008004100000245006700041210006400108300001300172490000600185520073100191100002400922856004600946       2014                        eng d00aUsing t-closeness anonymity to control for non-discrimination.0 aUsing tcloseness anonymity to control for nondiscrimination  a99–1290 v73 aWe 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.1 aRuggieri, Salvatore  uhttp://dl.acm.org/citation.cfm?id=2870623