Privacy Preserving OLAP Data Cubes

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.



Titolo: Privacy Preserving OLAP Data Cubes
Autori: 
CUZZOCREA, Alfredo Massimiliano
Data di pubblicazione: 2014
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.
Handle: http://hdl.handle.net/11368/2853918
ISBN: 9781466652026
Appare nelle tipologie:2.1 Contributo in Volume (Capitolo,Saggio)

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