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EnglishIt 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.
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| Titolo: | Privacy Preserving OLAP Data Cubes | |
| Autori: | ||
| 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/20.500.11770/312624 | |
| ISBN: | 9781466652026 | |
| Appare nelle tipologie: | 2.1 Contributo in volume (Capitolo o Saggio) |
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