An Enhanced Model for Privacy Preserving Data Publication

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Provided by: International Journal of Advanced Engineering Applications (IJAEA)
Topic: Data Management
Format: PDF
The success of data mining not only relies on the availability of high quality data but also in preserving person specific and sensitive information from the threat of identity disclosure. Recent papers on the available anonymization techniques like bucketization and generalization has proved that they do not prevent membership disclosure and it is also not suitable in the case of high dimensional data. In this paper, the authors propose a new notion of privacy known as slicing which partitions the data both horizontally and vertically.
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