Using t-closeness anonymity to control for non-discrimination.

You are here

TitleUsing t-closeness anonymity to control for non-discrimination.
Publication TypeJournal Article
Year of Publication2014
AuthorsRuggieri, S
JournalTrans. Data Privacy
Volume7
Issue2
Pagination99–129
AbstractWe 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.
URLhttp://dl.acm.org/citation.cfm?id=2870623
Research Line: