Preserving confidentiality of high-dimensional tabulated data: Statistical and computational issues

被引：23

作者：

Dobra, A ^{[1
]}

Karr, AF ^{[1
]}

Sanil, AP ^{[1
]}

机构：

[1] Natl Inst Stat Sci, Res Triangle Pk, NC 27709 USA

来源：

STATISTICS AND COMPUTING | 2003年 / 13卷 / 04期

基金：

美国国家科学基金会;

关键词：

branch and bound; contingency tables; disclosure limitation; integer programming; marginal bounds; shuttle algorithm;

D O I：

10.1023/A:1025671023941

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Dissemination of information derived from large contingency tables formed from confidential data is a major responsibility of statistical agencies. In this paper we present solutions to several computational and algorithmic problems that arise in the dissemination of cross-tabulations (marginal sub-tables) from a single underlying table. These include data structures that exploit sparsity to support efficient computation of marginals and algorithms such as iterative proportional fitting, as well as a generalized form of the shuttle algorithm that computes sharp bounds on (small, confidentiality threatening) cells in the full table from arbitrary sets of released marginals. We give examples illustrating the techniques.

引用

页码：363 / 370

页数：8

共 9 条

[1] Bounds for cell entries in contingency tables given marginal totals and decomposable graphs [J].