It has been demonstrated that malicious users can infer sensitive knowledge from online corporate databases and data cubes that do not adopt effective privacy preserving countermeasures. From this breaking evidence, a plethora of Privacy Preserving Data Mining (PPDM) techniques has been proposed during the last years. Each of these techniques focuses on supporting the privacy preservation of a specialized KDD/DM task such as frequent item set mining, clustering etc. Privacy Preserving OLAP (PPOLAP) is a specific PPDM technique dealing with the privacy preservation of data cubes.
Privacy Preserving OLAP Data Cubes
CUZZOCREA, Alfredo Massimiliano
2014-01-01
Abstract
It has been demonstrated that malicious users can infer sensitive knowledge from online corporate databases and data cubes that do not adopt effective privacy preserving countermeasures. From this breaking evidence, a plethora of Privacy Preserving Data Mining (PPDM) techniques has been proposed during the last years. Each of these techniques focuses on supporting the privacy preservation of a specialized KDD/DM task such as frequent item set mining, clustering etc. Privacy Preserving OLAP (PPOLAP) is a specific PPDM technique dealing with the privacy preservation of data cubes.File in questo prodotto:
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