Articles in Press

Accepted articles here

Latest Issues

Volume 17 Issue 3
Volume 17 Issue 2
Volume 17 Issue 1

Volume 16 Issue 3
Volume 16 Issue 2
Volume 16 Issue 1

Volume 15 Issue 3
Volume 15 Issue 2
Volume 15 Issue 1

Volume 14 Issue 3
Volume 14 Issue 2
Volume 14 Issue 1

Volume 13 Issue 3
Volume 13 Issue 2
Volume 13 Issue 1

Volume 12 Issue 3
Volume 12 Issue 2
Volume 12 Issue 1

Volume 11 Issue 3
Volume 11 Issue 2
Volume 11 Issue 1

Volume 10 Issue 3
Volume 10 Issue 2
Volume 10 Issue 1

Volume 9 Issue 3
Volume 9 Issue 2
Volume 9 Issue 1

Volume 8 Issue 3
Volume 8 Issue 2
Volume 8 Issue 1

Volume 7 Issue 3
Volume 7 Issue 2
Volume 7 Issue 1

Volume 6 Issue 3
Volume 6 Issue 2
Volume 6 Issue 1

Volume 5 Issue 3
Volume 5 Issue 2
Volume 5 Issue 1

Volume 4 Issue 3
Volume 4 Issue 2
Volume 4 Issue 1

Volume 3 Issue 3
Volume 3 Issue 2
Volume 3 Issue 1

Volume 2 Issue 3
Volume 2 Issue 2
Volume 2 Issue 1

Volume 1 Issue 3
Volume 1 Issue 2
Volume 1 Issue 1

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.

Follow us

Supports