Inference Control in Statistical Databases
From Theory to Practice
Published on 17. April 2002
Book
Paperback/Softback
VIII, 231 pages
978-3-540-43614-0 (ISBN)
Description
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.
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.
More details
Series
Edition
2002 ed.
Language
English
Place of publication
Berlin
Germany
Publishing group
Springer Berlin
Target group
Professional and scholarly
Research
Illustrations
VIII, 231 p.
Dimensions
Height: 235 mm
Width: 155 mm
Thickness: 14 mm
Weight
376 gr
ISBN-13
978-3-540-43614-0 (9783540436140)
DOI
10.1007/3-540-47804-3
Schweitzer Classification
Other editions
Additional editions
E-Book
08/2003
Springer
€53.49
Available for download
Person
Content
Advances in Inference Control in Statistical Databases: An Overview.- Advances in Inference Control in Statistical Databases: An Overview.- Tabular Data Protection.- Cell Suppression: Experience and Theory.- Bounds on Entries in 3-Dimensional Contingency Tables Subject to Given Marginal Totals.- Extending Cell Suppression to Protect Tabular Data against Several Attackers.- Network Flows Heuristics for Complementary Cell Suppression: An Empirical Evaluation and Extensions.- HiTaS: A Heuristic Approach to Cell Suppression in Hierarchical Tables.- Microdata Protection.- Model Based Disclosure Protection.- Microdata Protection through Noise Addition.- Sensitive Micro Data Protection Using Latin Hypercube Sampling Technique.- Integrating File and Record Level Disclosure Risk Assessment.- Disclosure Risk Assessment in Perturbative Microdata Protection.- LHS-Based Hybrid Microdata vs Rank Swapping and Microaggregation for Numeric Microdata Protection.- Post-Masking Optimization of the Tradeoff between Information Loss and Disclosure Risk in Masked Microdata Sets.- Software and User Case Studies.- The CASC Project.- Tools and Strategies to Protect Multiple Tables with the GHQUAR Cell Suppression Engine.- SDC in the 2000 U.S. Decennial Census.- Applications of Statistical Disclosure Control at Statistics Netherlands.- Empirical Evidences on Protecting Population Uniqueness at Idescat.