Document - Data anonimity meets non-discrimination

2013

Conference article Open Access

Data anonimity meets non-discrimination

Ruggieri S.

Privacy Discrimination H.2.8 Database Applications

We investigate the relation between t-closeness, a well-known model of data anonymization, and alpha-protection, a model of data discrimination. We show that t-closeness implies bd(t)-protection, for a bound function bd() depending on the discrimination measure at hand. This allows us to adapt an inference control method, the Mondrian multidimensional generalization technique, 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 nondiscrimination research in data mining.

Source: ICDMW 2013 - IEEE 13th International Conference on Data Mining Workshops, pp. 875–882, Dallas, Texas, USA, 7-10 December 2013

Publisher: IEEE, New York, USA

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@inproceedings{oai:it.cnr:prodotti:326360,
	title = {Data anonimity meets non-discrimination},
	author = {Ruggieri S.},
	publisher = {IEEE, New York, USA},
	doi = {10.1109/icdmw.2013.56},
	booktitle = {ICDMW 2013 - IEEE 13th International Conference on Data Mining Workshops, pp. 875–882, Dallas, Texas, USA, 7-10 December 2013},
	year = {2013}
}