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| Utility-based Microdata \emph{k}-Anonymity Algorithm |
| Pingshui Wang |
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Abstract Anonymization is one of the primary techniques realizing privacy
preserving microdata release. At present most anonymization methods
ignore the utility influence relation between
quasi-identifier\linebreak attributes and sensitive attributes,
which result in high information loss and low utility. To address
this problem, this paper laid great stress on data utility and
created a utility influence matrix of quasi-identifier attributes on
sensitive attributes, and designed a heuristic anonymity model and
algorithm using clustering techniques. Moreover, the paper executed
experimental verification on real dataset and carried out
contrastive analysis with classical anonymity algorithms.
Experimental results show that the proposed algorithm can improve
the usability of the released data while satisfying privacy
preserving requirements.
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