When publishing contingency tables which contain official statistics, a need to preserve statistical confidentiality arises. Statistical disclosure of individual units must be prevented. There is a wide choice of techniques to achieve this anonymization: cell supression, cell perturbation, etc. In this paper, we tackle the problem of using anonymized data to compute exact statistics; our approach is based on privacy homomorphisms, which are encryption transformations such that the decryption of a function of cyphers is a (possibly different) function of the corresponding clear messages. A new privacy homomorphism is presented and combined with some anonymization techniques, in order for a classified level to retrieve exact statistics from statistics computed on disclosure-protected data at an unclassified level.
@article{urn:eudml:doc:40194,
title = {Privacy homomorphisms for statistical confidentiality.},
journal = {Q\"uestii\'o},
volume = {20},
year = {1996},
pages = {505-521},
zbl = {1167.68372},
language = {en},
url = {http://dml.mathdoc.fr/item/urn:eudml:doc:40194}
}
Domingo i Ferrer, Josep. Privacy homomorphisms for statistical confidentiality.. Qüestiió, Tome 20 (1996) pp. 505-521. http://gdmltest.u-ga.fr/item/urn:eudml:doc:40194/