Journal ArticleDOI
Linear sensitivity measures in statistical disclosure control
Reads0
Chats0
TLDR
In this article, the mathematical properties of a class of functions called linear sensitivity measures are investigated for maintaining the statistical confidentiality of respondents to a census or statistical survey such as an establishment-based economic survey.About:
This article is published in Journal of Statistical Planning and Inference.The article was published on 1981-01-01. It has received 40 citations till now.read more
Citations
More filters
Journal ArticleDOI
Suppression Methodology and Statistical Disclosure Control
TL;DR: In this paper, the authors discuss theory and method of complementary cell suppression and related topics in statistical disclosure control, focusing on the development of methods that are theoretically broad but also practical to implement.
Journal ArticleDOI
Disclosure-Limited Data Dissemination
George T. Duncan,Diane Lambert +1 more
TL;DR: Common disclosure control policies, such as requiring released cell relative frequencies to be bounded away from both zero and one, are shown to be equivalent to disclosure rules that allow data release only if specific uncertainty functions at particular predictive distribution are allowed.
Processing of Erroneous and Unsafe Data
TL;DR: In het proefschrift wordt ingegaan op de wiskundige problemen die het beveiligen van gevoelige gegevens met zich mee brengt, worden oplossingen beschreven.
Journal ArticleDOI
Network Models for Complementary Cell Suppression
TL;DR: These methods are shown to be optimal for certain problems and to offer theoretical and practical advantages, including comprehensiveness, comprehensibleness, and computational efficiency, based on linear optimization over a mathematical network.
Journal ArticleDOI
Optimal noise addition for preserving confidentiality in multivariate data
TL;DR: These measures of confidentiality and data integrity for multivariate data when noise has been added are evaluated in the case that the vectors of added noise have independent components and it is demonstrated that the amount of protection provided could be relatively low for data of high dimension.