<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Statistical agencies are making increased use of the internet to disseminate census tabular outputs through web-based flexible table-generating servers that allow users to define and generate their own tables. The key questions in the development of these servers are: (1) what data should be used to generate the tables, and (2) what statistical disclosure control (SDC) method should be applied. To generate flexible tables, the server has to be able to measure the disclosure risk in the final output table, apply the SDC method and then iteratively reassess the disclosure risk. SDC methods may be applied either to the underlying data used to generate the tables and/or to the final output table that is generated from original data. Besides assessing disclosure risk, the server should provide a measure of data utility by comparing the perturbed table to the original table. In this article, we examine aspects of the design and development of a flexible table-generating server for census tables and demonstrate a disclosure risk-data utility analysis for comparing SDC methods. We propose measures for disclosure risk and data utility that are based on information theory.</p></abstract>

Statistical agencies are making increased use of the internet to disseminate census tabular outputs through web-based flexible table-generating servers that allow users to define and generate their own tables. The key questions in the development of these servers are: (1) what data should be used to generate the tables, and (2) what statistical disclosure control (SDC) method should be applied. To generate flexible tables, the server has to be able to measure the disclosure risk in the final output table, apply the SDC method and then iteratively reassess the disclosure risk. SDC methods may be applied either to the underlying data used to generate the tables and/or to the final output table that is generated from original data. Besides assessing disclosure risk, the server should provide a measure of data utility by comparing the perturbed table to the original table. In this article, we examine aspects of the design and development of a flexible table-generating server for census tables and demonstrate a disclosure risk-data utility analysis for comparing SDC methods. We propose measures for disclosure risk and data utility that are based on information theory.

Measuring Disclosure Risk and Data Utility for Flexible Table Generators

University of Manchester, Social Statistics, Humanities Bridgeford Street, Manchester M13 9PL, United Kingdom.

University of Manchester, Social Statistics, Humanities Bridgeford Street, Manchester M13 9PL, United Kingdom

Journal of Official Statistics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Measuring Disclosure Risk and Data Utility for Flexible Table Generators"}

Statistical agencies are making increased use of the internet to disseminate census tabular outputs through web-based flexible table-generating servers that...

Measuring Disclosure Risk and Data Utility for Flexible Table Generators

Online veröffentlicht: 27. Juni 2015

Seitenbereich: 305 - 324

Eingereicht: 01. Juli 2013

Akzeptiert: 01. Nov. 2014

DOI: https://doi.org/10.1515/jos-2015-0019

Schlüsselwörter
Statistical disclosure control, census tabular data, entropy, Hellinger distance

© by Natalie Shlomo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Measuring Disclosure Risk and Data Utility for Flexible Table Generators

Online veröffentlicht: 27. Juni 2015

Seitenbereich: 305 - 324

Eingereicht: 01. Juli 2013

Akzeptiert: 01. Nov. 2014

DOI: https://doi.org/10.1515/jos-2015-0019

SchlüsselwörterStatistical disclosure control, census tabular data, entropy, Hellinger distance

© by Natalie Shlomo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
Statistical disclosure control, census tabular data, entropy, Hellinger distance