A posteriori disclosure risk measure for tabular data based on conditional entropy.

Oganian, Anna, and Domingo-Ferrer, Josep. "A posteriori disclosure risk measure for tabular data based on conditional entropy.." SORT 27.2 (2003): 175-190. <http://eudml.org/doc/40439>.

@article{Oganian2003,
abstract = {Statistical database protection, also known as Statistical Disclosure Control (SDC), is a part of information security which tries to prevent published statistical information (tables, individual records) from disclosing the contribution of specific respondents. This paper deals with the assessment of the disclosure risk associated to the release of tabular data. So-called sensitivity rules are currently being used to measure the disclosure risk for tables. This rules operate on an a priori basis: the data are examined and the rules are used to decide whether the data can be released as they stand or should rather be protected. In this paper, we propose to complement a priori risk assessment with a posteriori risk assessment in order to achieve a higher level of security, that is, we propose to take the protected information into account when measuring the disclosure risk.The proposed a posteriori disclosure risk measure is compatible with a broad class of disclosure protection methods and can be extended for computing disclosure risk for a set of linked tables. In the case of linked table protection via cell supression, the proposed measure allows detection of secondary supression patterns which offer more protection than others.},
author = {Oganian, Anna, Domingo-Ferrer, Josep},
journal = {SORT},
keywords = {Bases de datos; Datos estadísticos; Protección de datos; Entropía; statistical disclosure control; statistical databases; tabular data; security},
language = {eng},
number = {2},
pages = {175-190},
title = {A posteriori disclosure risk measure for tabular data based on conditional entropy.},
url = {http://eudml.org/doc/40439},
volume = {27},
year = {2003},
}

TY - JOUR
AU - Oganian, Anna
AU - Domingo-Ferrer, Josep
TI - A posteriori disclosure risk measure for tabular data based on conditional entropy.
JO - SORT
PY - 2003
VL - 27
IS - 2
SP - 175
EP - 190
AB - Statistical database protection, also known as Statistical Disclosure Control (SDC), is a part of information security which tries to prevent published statistical information (tables, individual records) from disclosing the contribution of specific respondents. This paper deals with the assessment of the disclosure risk associated to the release of tabular data. So-called sensitivity rules are currently being used to measure the disclosure risk for tables. This rules operate on an a priori basis: the data are examined and the rules are used to decide whether the data can be released as they stand or should rather be protected. In this paper, we propose to complement a priori risk assessment with a posteriori risk assessment in order to achieve a higher level of security, that is, we propose to take the protected information into account when measuring the disclosure risk.The proposed a posteriori disclosure risk measure is compatible with a broad class of disclosure protection methods and can be extended for computing disclosure risk for a set of linked tables. In the case of linked table protection via cell supression, the proposed measure allows detection of secondary supression patterns which offer more protection than others.
LA - eng
KW - Bases de datos; Datos estadísticos; Protección de datos; Entropía; statistical disclosure control; statistical databases; tabular data; security
UR - http://eudml.org/doc/40439
ER -