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.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:573575 |
| Date | January 2011 |
| Creators | Serpell, Martin Craig |
| Publisher | University of the West of England, Bristol |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
Page generated in 0.0021 seconds