research-article

Scaling up a hybrid genetic linear programming algorithm for statistical disclosure control

Authors:

Martin C. Serpell,

James E. Smith,

Alistair R. Clark,

Andrea T. StaggemeierAuthors Info & Claims

GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation

Pages 1675 - 1682

https://doi.org/10.1145/2001576.2001802

Published: 12 July 2011 Publication History

Abstract

This paper looks at the real world problem of statistical disclosure control. National Statistics Agencies are required to publish detailed statistics and simultaneously guarantee the confidentiality of the contributors. When published statistical tables contain magnitude data such as turnover or health statistics the preferred method is to suppress the values of cells which may reveal confidential information. However suppressing these 'primary' cells alone will not guarantee protection due the presence of margin (row/column) totals and therefore other 'secondary' cells must also be suppressed. A previously developed algorithm that hybridizes linear programming with a genetic algorithm has been shown to protect tables with up to 40,000 cells, however Statistical Agencies are often required to protect tables with over 100,000 cells.

This algorithm's performance highly depended on the choice of mutation operator so firstly this dependency was removed. As the algorithm is unable to protect larger tables due to the time it takes for its fitness function (a linear program) to execute a series of modifications have been applied. These modifications significantly reduced its execution time which in turn greatly extend the capabilities of the hybrid algorithm to the point that it can now protect tables with up to one million cells.

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Cited By

Serpell MSmith JAlba E(2013)Initial application of ant colony optimisation to statistical disclosure controlProceedings of the 15th annual conference on Genetic and evolutionary computation10.1145/2463372.2463386(97-104)Online publication date: 6-Jul-2013
https://dl.acm.org/doi/10.1145/2463372.2463386

Index Terms

Scaling up a hybrid genetic linear programming algorithm for statistical disclosure control
1. Mathematics of computing
  1. Mathematical analysis
    1. Mathematical optimization
2. Theory of computation
  1. Design and analysis of algorithms
    1. Mathematical optimization

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cover image ACM Conferences

GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation

July 2011

2140 pages

ISBN:9781450305570

DOI:10.1145/2001576

Editor:
Natalio Krasnogor
University of Nottingham, UK
,
General Chair:
Pier Luca Lanzi
Politecnico di Milano, Italy

Copyright © 2011 ACM.

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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 July 2011

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GECCO '11

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SIGEVO

GECCO '11: Genetic and Evolutionary Computation Conference

July 12 - 16, 2011

Dublin, Ireland

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Cited By

Serpell MSmith JAlba E(2013)Initial application of ant colony optimisation to statistical disclosure controlProceedings of the 15th annual conference on Genetic and evolutionary computation10.1145/2463372.2463386(97-104)Online publication date: 6-Jul-2013
https://dl.acm.org/doi/10.1145/2463372.2463386

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