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

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.
9781466652026
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11368/2853918
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact