The problem of computing privacy preserving OLAP data cubes is gaining momentum in the Data Mining and Warehousing research community, due to the large spectrum of application scenarios where OLAP and, under a larger vision, Business Intelligence (BI) are exploited successfully. Following this emerging trend, several privacy preserving OLAP techniques have been proposed recently, with alternate fortune. This research proposes an excerpt of two significant state-of-the-art contributions in the contexts of centralized and distributed privacy preserving OLAP research, by providing several case studies showing challenges and achievements of these contributions, along with directions for future efforts in these fields © 2011 MIPRO.
Privacy preserving OLAP: Models, issues, algorithms
Cuzzocrea;Alfredo
2011
Abstract
The problem of computing privacy preserving OLAP data cubes is gaining momentum in the Data Mining and Warehousing research community, due to the large spectrum of application scenarios where OLAP and, under a larger vision, Business Intelligence (BI) are exploited successfully. Following this emerging trend, several privacy preserving OLAP techniques have been proposed recently, with alternate fortune. This research proposes an excerpt of two significant state-of-the-art contributions in the contexts of centralized and distributed privacy preserving OLAP research, by providing several case studies showing challenges and achievements of these contributions, along with directions for future efforts in these fields © 2011 MIPRO.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
