Inference Control in Statistical Databases

From Theory to Practice

By: Josep Domingo-Ferrer (Editor)

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Paperback | 17 April 2002

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Inference control in statistical databases, also known as statistical disclosure limitation or statistical confidentiality, is about finding tradeoffs to the tension between the increasing societal need for accurate statistical data and the legal and ethical obligation to protect privacy of individuals and enterprises which are the source of data for producing statistics. Techniques used by intruders to make inferences compromising privacy increasingly draw on data mining, record linkage, knowledge discovery, and data analysis and thus statistical inference control becomes an integral part of computer science.
This coherent state-of-the-art survey presents some of the most recent work in the field. The papers presented together with an introduction are organized in topical sections on tabular data protection, microdata protection, and software and user case studies.

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Advances in Inference Control in Statistical Databases: An Overview	p. 1
Tabular Data Protection
Cell Suppression: Experience and Theory	p. 8
Bounds on Entries in 3-Dimensional Contingency Tables Subject to Given Marginal Totals	p. 21
Extending Cell Suppression to Protect Tabular Data against Several Attackers	p. 34
Network Flows Heuristics for Complementary Cell Suppression: An Empirical Evaluation and Extensions	p. 59
HiTaS: A Heuristic Approach to Cell Suppression in Hierarchical Tables	p. 74
Microdata Protection
Model Based Disclosure Protection	p. 83
Microdata Protection through Noise Addition	p. 97
Sensitive Micro Data Protection Using Latin Hypercube Sampling Technique	p. 117
Integrating File and Record Level Disclosure Risk Assessment	p. 126
Disclosure Risk Assessment in Perturbative Microdata Protection	p. 135
LHS-Based Hybrid Microdata vs Rank Swapping and Microaggregation for Numeric Microdata Protection	p. 153
Post-Masking Optimization of the Tradeoff between Information Loss and Disclosure Risk in Masked Microdata Sets	p. 163
Software and User Case Studies
The CASC Project	p. 172
Tools and Strategies to Protect Multiples Tables with the GHQUAR Cell Suppression Engine	p. 181
SDC in the 2000 U.S. Decennial Census	p. 193
Applications of Statistical Disclosure Control at Statistics Netherlands	p. 203
Empirical Evidences on Protecting Population Uniqueness at Idescat	p. 213
Author Index	p. 231
Table of Contents provided by Publisher. All Rights Reserved.

Inference Control in Statistical Databases

From Theory to Practice

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