Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.573575
Title: Mathematical modelling and artificial intelligence applied to statistical disclosure control
Author: Serpell, Martin Craig
Awarding Body: University of the West of England, Bristol
Current Institution: University of the West of England, Bristol
Date of Award: 2011
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Abstract:
This thesis looks at the problem of protecting large published statistical tables using cell suppression. Optimal cell suppression has been shown to be NP-Hard and can therefore only be applied to small tables. Using heuristic techniques to protect large tables tends to suppress far too many table cells lessening the utility of the table. Current state-of-the- art cell suppression algorithms can protect statistical tables with up to forty thousand cells. In this thesis a new model is derived that can fully protect statistical tables with up to one million cells without excessive over-suppression. This has been achieved by creating a new mathematical model that can protect cells in groups rather than individually. A pre-processing step was also introduced to reduce the number of cells that actually need to be protected. Further improvements have been gained through the employment of a self-adaptive Genetic Algorithm to optimise the order in which the groups of cells are protected and the employment of a surrogate fitness function to reduce execution time.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.573575  DOI: Not available
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