Privacy homomorphisms for statistical confidentiality.

Domingo i Ferrer, Josep. "Privacy homomorphisms for statistical confidentiality.." Qüestiió 20.3 (1996): 505-521. <http://eudml.org/doc/40194>.

@article{DomingoiFerrer1996,
abstract = {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.},
author = {Domingo i Ferrer, Josep},
journal = {Qüestiió},
keywords = {Homomorfismos; Protección de datos; Protección informática; Datos estadísticos; Codificación de datos; Criptología; statistical confidentiality; privacy homomorphisms},
language = {eng},
number = {3},
pages = {505-521},
title = {Privacy homomorphisms for statistical confidentiality.},
url = {http://eudml.org/doc/40194},
volume = {20},
year = {1996},
}

TY - JOUR
AU - Domingo i Ferrer, Josep
TI - Privacy homomorphisms for statistical confidentiality.
JO - Qüestiió
PY - 1996
VL - 20
IS - 3
SP - 505
EP - 521
AB - 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.
LA - eng
KW - Homomorfismos; Protección de datos; Protección informática; Datos estadísticos; Codificación de datos; Criptología; statistical confidentiality; privacy homomorphisms
UR - http://eudml.org/doc/40194
ER -