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Volume 7 Issue 2

Using t-closeness anonymity to control for non-discrimination

Salvatore Ruggieri^(a),(*)

Transactions on Data Privacy 7:2 (2014) 99 - 129

Abstract, PDF

(a) Dipartimento di Informatica, Universit `a di Pisa, Largo B. Pontecorvo 3, 56127 Pisa, Italy.

e-mail:ruggieri @di.unipi.it

Abstract

We investigate the relation between t-closeness, a well-known model of data anonymization against attribute disclosure, and a-protection, a model of the social discrimination hidden in data. We show that t-closeness implies bd_f(t)-protection, for a bound function bd_f() 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.

^* Corresponding author.

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