Privacy Preservation for Inference Control with Improved ND Algorithm
Maulik Joshi, Chetna Chand
Privacy, preserving, OLAP, inference problem
Online analytical processing (OLAP) is providing functionality of analysis of multidimensional data cube and it is support decision making and knowledge discovery technique. OLAP operation on such databases may reveal the information which is private to an individual. Privacy preserving is important in OLAP because private information can be shown through user query. So adversarial inference of private information its main issue in OLAP. Privacy preserving OLAP had focused on single aggregate function but which eliminates from consideration an important class of privacy breaches where partial information, but not exact values, of private data is disclosed .In this paper proposed technique provide protection for exact and partial disclosure in OLAP with more than one aggregate function with reducing processing time of OLAP cube.
Article Details
Unique Paper ID: 143630

Publication Volume & Issue: Volume 2, Issue 12

Page(s): 99 - 103
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